【2011-2016】 NIPS彙總 - Advances in Neural Information Processing Systems

Advances in Neural Information Processing Systems 24 (NIPS 2011)

The papers below appear in Advances in Neural Information Processing Systems 24 edited by  J. Shawe-Taylor  and  R.S. Zemel  and  P.L. Bartlett and  F. Pereira  and  K.Q. Weinberger .
They are proceedings from the conference, "Neural Information Processing Systems 2011."

• Maximum Margin Multi-Instance Learning Hua Wang, Heng Huang, Farhad Kamangar, Feiping Nie, Chris H. Ding
• Shaping Level Sets with Submodular Functions Francis R. Bach
• Nonlinear Inverse Reinforcement Learning with Gaussian Processes Sergey Levine, Zoran Popovic, Vladlen Koltun
• Video Annotation and Tracking with Active Learning Carl Vondrick, Deva Ramanan
• On U-processes and clustering performance Stéphan J. Clémençcon
• Penalty Decomposition Methods for Rank Minimization Yong Zhang, Zhaosong Lu
• Sparse Manifold Clustering and Embedding Ehsan Elhamifar, René Vidal
• Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction Siwei Lyu
• Image Parsing with Stochastic Scene Grammar Yibiao Zhao, Song-chun Zhu
• A Reinforcement Learning Theory for Homeostatic Regulation Mehdi Keramati, Boris S. Gutkin
• Learning large-margin halfspaces with more malicious noise Phil Long, Rocco Servedio
• On Strategy Stitching in Large Extensive Form Multiplayer Games Richard G. Gibson, Duane Szafron
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials Philipp Krähenbühl, Vladlen Koltun
• Transfer Learning by Borrowing Examples for Multiclass Object Detection Joseph J. Lim, Ruslan R. Salakhutdinov, Antonio Torralba
• Environmental statistics and the trade-off between model-based and TD learning in humans Dylan A. Simon, Nathaniel D. Daw
• Variational Learning for Recurrent Spiking Networks Danilo J. Rezende, Daan Wierstra, Wulfram Gerstner
• Multiple Instance Learning on Structured Data Dan Zhang, Yan Liu, Luo Si, Jian Zhang, Richard D. Lawrence
• Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds Nitesh Shroff, Pavan Turaga, Rama Chellappa
• A Global Structural EM Algorithm for a Model of Cancer Progression Ali Tofigh, Erik Sj̦lund, Mattias H̦glund, Jens Lagergren
• Action-Gap Phenomenon in Reinforcement Learning Amir-massoud Farahmand
• Generalized Lasso based Approximation of Sparse Coding for Visual Recognition Nobuyuki Morioka, Shin'ichi Satoh
• Matrix Completion for Multi-label Image Classification Ricardo S. Cabral, Fernando Torre, Joao P. Costeira, Alexandre Bernardino
• Multi-View Learning of Word Embeddings via CCA Paramveer Dhillon, Dean P. Foster, Lyle H. Ungar
• Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent Shinichi Nakajima, Masashi Sugiyama, S. D. Babacan
• Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron Ryota Kobayashi, Yasuhiro Tsubo, Petr Lansky, Shigeru Shinomoto
• Additive Gaussian Processes David K. Duvenaud, Hannes Nickisch, Carl E. Rasmussen
• Inferring Interaction Networks using the IBP applied to microRNA Target Prediction Hai-son P. Le, Ziv Bar-joseph
• Semantic Labeling of 3D Point Clouds for Indoor Scenes Hema S. Koppula, Abhishek Anand, Thorsten Joachims, Ashutosh Saxena
• Learning Higher-Order Graph Structure with Features by Structure Penalty Shilin Ding, Grace Wahba, Xiaojin Zhu
• Analysis and Improvement of Policy Gradient Estimation Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama
• Dimensionality Reduction Using the Sparse Linear Model Ioannis A. Gkioulekas, Todd Zickler
• Robust Multi-Class Gaussian Process Classification Daniel Hernández-lobato, Jose M. Hernández-lobato, Pierre Dupont
• Maximum Margin Multi-Label Structured Prediction Christoph H. Lampert
• Extracting Speaker-Specific Information with a Regularized Siamese Deep Network Ke Chen, Ahmad Salman
• Thinning Measurement Models and Questionnaire Design Ricardo Silva
• Inductive reasoning about chimeric creatures Charles Kemp
• Optimal Reinforcement Learning for Gaussian Systems Philipp Hennig
• A Denoising View of Matrix Completion Weiran Wang, Miguel Á. Carreira-Perpiñán, Zhengdong Lu
• Efficient Online Learning via Randomized Rounding Nicolò Cesa-bianchi, Ohad Shamir
• Efficient Methods for Overlapping Group Lasso Lei Yuan, Jun Liu, Jieping Ye
• Differentially Private M-Estimators Jing Lei
• Multiple Instance Filtering Kamil A. Wnuk, Stefano Soatto
• Phase transition in the family of p-resistances Morteza Alamgir, Ulrike V. Luxburg
• Convergent Bounds on the Euclidean Distance Yoonho Hwang, Hee-kap Ahn
• Heavy-tailed Distances for Gradient Based Image Descriptors Yangqing Jia, Trevor Darrell
• RTRMC: A Riemannian trust-region method for low-rank matrix completion Nicolas Boumal, Pierre-antoine Absil
• Expressive Power and Approximation Errors of Restricted Boltzmann Machines Guido F. Montufar, Johannes Rauh, Nihat Ay
• History distribution matching method for predicting effectiveness of HIV combination therapies Jasmina Bogojeska
• Semi-supervised Regression via Parallel Field Regularization Binbin Lin, Chiyuan Zhang, Xiaofei He
• Object Detection with Grammar Models Ross B. Girshick, Pedro F. Felzenszwalb, David A. McAllester
• Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning Eric Moulines, Francis R. Bach
• On fast approximate submodular minimization Stefanie Jegelka, Hui Lin, Jeff A. Bilmes
• Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits Matthias S. Keil
• Efficient anomaly detection using bipartite k-NN graphs Kumar Sricharan, Alfred O. Hero
• Projection onto A Nonnegative Max-Heap Jun Liu, Liang Sun, Jieping Ye
• Improving Topic Coherence with Regularized Topic Models David Newman, Edwin V. Bonilla, Wray Buntine
• A Two-Stage Weighting Framework for Multi-Source Domain Adaptation Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, Jieping Ye
• An ideal observer model for identifying the reference frame of objects Joseph L. Austerweil, Abram L. Friesen, Thomas L. Griffiths
• Generalized Beta Mixtures of Gaussians Artin Armagan, Merlise Clyde, David B. Dunson
• Large-Scale Sparse Principal Component Analysis with Application to Text Data Youwei Zhang, Laurent E. Ghaoui
• Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC Trung T. Pham, Tat-jun Chin, Jin Yu, David Suter
• \theta-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding Congcong Li, Ashutosh Saxena, Tsuhan Chen
• Crowdclustering Ryan G. Gomes, Peter Welinder, Andreas Krause, Pietro Perona
• Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition Jia Deng, Sanjeev Satheesh, Alexander C. Berg, Fei Li
• Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification Ichiro Takeuchi, Masashi Sugiyama
• The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers Luca Oneto, Davide Anguita, Alessandro Ghio, Sandro Ridella
• Relative Density-Ratio Estimation for Robust Distribution Comparison Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama
• Solving Decision Problems with Limited Information Denis D. Maua, Cassio Campos
• Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation Zhouchen Lin, Risheng Liu, Zhixun Su
• Learning a Tree of Metrics with Disjoint Visual Features Kristen Grauman, Fei Sha, Sung Ju Hwang
• Efficient inference in matrix-variate Gaussian models with \iid observation noise Oliver Stegle, Christoph Lippert, Joris M. Mooij, Neil D. Lawrence, Karsten M. Borgwardt
• On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij, Dominik Janzing, Tom Heskes, Prof. Bernhard Schölkopf
• Learning to Agglomerate Superpixel Hierarchies Viren Jain, Srinivas C. Turaga, K Briggman, Moritz N. Helmstaedter, Winfried Denk, H. S. Seung
• A Convergence Analysis of Log-Linear Training Simon Wiesler, Hermann Ney
• Shallow vs. Deep Sum-Product Networks Olivier Delalleau, Yoshua Bengio
• Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment Sebastian A. Kurtek, Anuj Srivastava, Wei Wu
• From Bandits to Experts: On the Value of Side-Observations Shie Mannor, Ohad Shamir
• Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Benjamin Recht, Christopher Re, Stephen Wright, Feng Niu
• Clustered Multi-Task Learning Via Alternating Structure Optimization Jiayu Zhou, Jianhui Chen, Jieping Ye
• Why The Brain Separates Face Recognition From Object Recognition Joel Z. Leibo, Jim Mutch, Tomaso Poggio
• Reinforcement Learning using Kernel-Based Stochastic Factorization Andre S. Barreto, Doina Precup, Joelle Pineau
• k-NN Regression Adapts to Local Intrinsic Dimension Samory Kpotufe
• Learning unbelievable probabilities Xaq Pitkow, Yashar Ahmadian, Ken D. Miller
• A Machine Learning Approach to Predict Chemical Reactions Matthew A. Kayala, Pierre F. Baldi
• Dynamical segmentation of single trials from population neural data Biljana Petreska, Byron M. Yu, John P. Cunningham, Gopal Santhanam, Stephen I. Ryu, Krishna V. Shenoy, Maneesh Sahani
• Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance Carsten Rother, Martin Kiefel, Lumin Zhang, Prof. Bernhard Schölkopf, Peter V. Gehler
• Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations Emin Orhan, Robert A. Jacobs
• Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness Rémi Munos
• Reconstructing Patterns of Information Diffusion from Incomplete Observations Flavio Chierichetti, David Liben-nowell, Jon M. Kleinberg
• Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection Richard Socher, Eric H. Huang, Jeffrey Pennin, Christopher D. Manning, Andrew Y. Ng
• Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity Nir Ailon
• Modelling Genetic Variations using Fragmentation-Coagulation Processes Yee W. Teh, Charles Blundell, Lloyd Elliott
• Prediction strategies without loss Michael Kapralov, Rina Panigrahy
• Data Skeletonization via Reeb Graphs Xiaoyin Ge, Issam I. Safa, Mikhail Belkin, Yusu Wang
• Information Rates and Optimal Decoding in Large Neural Populations Kamiar R. Rad, Liam Paninski
• Selective Prediction of Financial Trends with Hidden Markov Models Dmitry Pidan, Ran El-Yaniv
• Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning Xinggang Wang, Xiang Bai, Xingwei Yang, Wenyu Liu, Longin J. Latecki
• Distributed Delayed Stochastic Optimization Alekh Agarwal, John C. Duchi
• Greedy Algorithms for Structurally Constrained High Dimensional Problems Ambuj Tewari, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction Elad Hazan, Satyen Kale
• Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries Zhen J. Xiang, Hao Xu, Peter J. Ramadge
• Minimax Localization of Structural Information in Large Noisy Matrices Mladen Kolar, Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh
• Maximum Covariance Unfolding : Manifold Learning for Bimodal Data Vijay Mahadevan, Chi W. Wong, Jose C. Pereira, Tom Liu, Nuno Vasconcelos, Lawrence K. Saul
• Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression Sham M. Kakade, Varun Kanade, Ohad Shamir, Adam Kalai
• On the Analysis of Multi-Channel Neural Spike Data Bo Chen, David E. Carlson, Lawrence Carin
• Learning Eigenvectors for Free Wouter M. Koolen, Wojciech Kotlowski, Manfred K. Warmuth
• Noise Thresholds for Spectral Clustering Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, Aarti Singh
• The Kernel Beta Process Lu Ren, Yingjian Wang, Lawrence Carin, David B. Dunson
• Statistical Performance of Convex Tensor Decomposition Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima
• Probabilistic amplitude and frequency demodulation Richard Turner, Maneesh Sahani
• Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators Dominique C. Perrault-joncas, Marina Meila
• Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons Yan Karklin, Eero P. Simoncelli
• Complexity of Inference in Latent Dirichlet Allocation David Sontag, Dan Roy
• ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning Quoc V. Le, Alexandre Karpenko, Jiquan Ngiam, Andrew Y. Ng
• Lower Bounds for Passive and Active Learning Maxim Raginsky, Alexander Rakhlin
• Stochastic convex optimization with bandit feedback Alekh Agarwal, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Alexander Rakhlin
• Structure Learning for Optimization Shulin Yang, Ali Rahimi
• Inverting Grice's Maxims to Learn Rules from Natural Language Extractions Mohammad S. Sorower, Janardhan R. Doppa, Walker Orr, Prasad Tadepalli, Thomas G. Dietterich, Xiaoli Z. Fern
• Active Classification based on Value of Classifier Tianshi Gao, Daphne Koller
• Group Anomaly Detection using Flexible Genre Models Liang Xiong, Barnabás Póczos, Jeff G. Schneider
• Approximating Semidefinite Programs in Sublinear Time Dan Garber, Elad Hazan
• SpaRCS: Recovering low-rank and sparse matrices from compressive measurements Andrew E. Waters, Aswin C. Sankaranarayanan, Richard Baraniuk
• Budgeted Optimization with Concurrent Stochastic-Duration Experiments Javad Azimi, Alan Fern, Xiaoli Z. Fern
• Online Submodular Set Cover, Ranking, and Repeated Active Learning Andrew Guillory, Jeff A. Bilmes
• Structured sparse coding via lateral inhibition Arthur D. Szlam, Karol Gregor, Yann L. Cun
• Sparse Filtering Jiquan Ngiam, Zhenghao Chen, Sonia A. Bhaskar, Pang W. Koh, Andrew Y. Ng
• Divide-and-Conquer Matrix Factorization Lester W. Mackey, Michael I. Jordan, Ameet Talwalkar
• Im2Text: Describing Images Using 1 Million Captioned Photographs Vicente Ordonez, Girish Kulkarni, Tamara L. Berg
• Nonstandard Interpretations of Probabilistic Programs for Efficient Inference David Wingate, Noah Goodman, Andreas Stuhlmueller, Jeffrey M. Siskind
• Collective Graphical Models Daniel R. Sheldon, Thomas G. Dietterich
• Metric Learning with Multiple Kernels Jun Wang, Huyen T. Do, Adam Woznica, Alexandros Kalousis
• ShareBoost: Efficient multiclass learning with feature sharing Shai Shalev-shwartz, Yonatan Wexler, Amnon Shashua
• Active dendrites: adaptation to spike-based communication Balazs B. Ujfalussy, Máté Lengyel
• Message-Passing for Approximate MAP Inference with Latent Variables Jiarong Jiang, Piyush Rai, Hal Daume
• A More Powerful Two-Sample Test in High Dimensions using Random Projection Miles Lopes, Laurent Jacob, Martin J. Wainwright
• Orthogonal Matching Pursuit with Replacement Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon
• Composite Multiclass Losses Elodie Vernet, Mark D. Reid, Robert C. Williamson
• Beating SGD: Learning SVMs in Sublinear Time Elad Hazan, Tomer Koren, Nati Srebro
• Greedy Model Averaging Dong Dai, Tong Zhang
• Large-Scale Category Structure Aware Image Categorization Bin Zhao, Fei Li, Eric P. Xing
• On the accuracy of l1-filtering of signals with block-sparse structure Fatma K. Karzan, Arkadi S. Nemirovski, Boris T. Polyak, Anatoli Juditsky
• Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach Qibin Zhao, Cesar F. Caiafa, Danilo P. Mandic, Liqing Zhang, Tonio Ball, Andreas Schulze-bonhage, Andrzej S. Cichocki
• Finite Time Analysis of Stratified Sampling for Monte Carlo Alexandra Carpentier, Rémi Munos
• Monte Carlo Value Iteration with Macro-Actions Zhan Lim, Lee Sun, David Hsu
• Structured Learning for Cell Tracking Xinghua Lou, Fred A. Hamprecht
• Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories Cristina Savin, Peter Dayan, Máté Lengyel
• Algorithms and hardness results for parallel large margin learning Phil Long, Rocco Servedio
• Portmanteau Vocabularies for Multi-Cue Image Representation Fahad S. Khan, Joost Weijer, Andrew D. Bagdanov, Maria Vanrell
• Boosting with Maximum Adaptive Sampling Charles Dubout, Francois Fleuret
• Gaussian Process Training with Input Noise Andrew Mchutchon, Carl E. Rasmussen
• Empirical models of spiking in neural populations Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
• Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities Angela Yao, Juergen Gall, Luc V. Gool, Raquel Urtasun
• Bayesian Partitioning of Large-Scale Distance Data David Adametz, Volker Roth
• From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models Skander Mensi, Richard Naud, Wulfram Gerstner
• On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference Guy Broeck
• Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices Xianxing Zhang, Lawrence Carin, David B. Dunson
• An Exact Algorithm for F-Measure Maximization Krzysztof J. Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
• Co-regularized Multi-view Spectral Clustering Abhishek Kumar, Piyush Rai, Hal Daume
• Sequence learning with hidden units in spiking neural networks Johanni Brea, Walter Senn, Jean-pascal Pfister
• Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis Shuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman
• A blind sparse deconvolution method for neural spike identification Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli
• How Do Humans Teach: On Curriculum Learning and Teaching Dimension Faisal Khan, Bilge Mutlu, Xiaojin Zhu
• Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization Mark Schmidt, Nicolas L. Roux, Francis R. Bach
• Joint 3D Estimation of Objects and Scene Layout Andreas Geiger, Christian Wojek, Raquel Urtasun
• Spatial distance dependent Chinese restaurant processes for image segmentation Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei
• Pylon Model for Semantic Segmentation Victor Lempitsky, Andrea Vedaldi, Andrew Zisserman
• t-divergence Based Approximate Inference Nan Ding, Yuan Qi, S.v.n. Vishwanathan
• Learning person-object interactions for action recognition in still images Vincent Delaitre, Josef Sivic, Ivan Laptev
• Submodular Multi-Label Learning James Petterson, Tibério S. Caetano
• Uniqueness of Belief Propagation on Signed Graphs Yusuke Watanabe
• Higher-Order Correlation Clustering for Image Segmentation Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli, Chang D. Yoo
• Optimal learning rates for least squares SVMs using Gaussian kernels Mona Eberts, Ingo Steinwart
• Learning Auto-regressive Models from Sequence and Non-sequence Data Tzu-kuo Huang, Jeff G. Schneider
• Committing Bandits Loc X. Bui, Ramesh Johari, Shie Mannor
• Energetically Optimal Action Potentials Martin B. Stemmler, Biswa Sengupta, Simon Laughlin, Jeremy Niven
• Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning Taiji Suzuki
• See the Tree Through the Lines: The Shazoo Algorithm Fabio Vitale, Nicolò Cesa-bianchi, Claudio Gentile, Giovanni Zappella
• The Fast Convergence of Boosting Matus J. Telgarsky
• Multi-armed bandits on implicit metric spaces Aleksandrs Slivkins
• Learning Anchor Planes for Classification Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari
• Infinite Latent SVM for Classification and Multi-task Learning Jun Zhu, Ning Chen, Eric P. Xing
• Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus
• Universal low-rank matrix recovery from Pauli measurements Yi-kai Liu
• Better Mini-Batch Algorithms via Accelerated Gradient Methods Andrew Cotter, Ohad Shamir, Nati Srebro, Karthik Sridharan
• Adaptive Hedge Tim V. Erven, Wouter M. Koolen, Steven D. Rooij, Peter Grünwald
• Agnostic Selective Classification Yair Wiener, Ran El-Yaniv
• Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs Armen Allahverdyan, Aram Galstyan
• PAC-Bayesian Analysis of Contextual Bandits Yevgeny Seldin, Peter Auer, John S. Shawe-taylor, Ronald Ortner, François Laviolette
• Bayesian Spike-Triggered Covariance Analysis Il Memming Park, Jonathan W. Pillow
• Non-conjugate Variational Message Passing for Multinomial and Binary Regression David A. Knowles, Tom Minka
• Learning to Search Efficiently in High Dimensions Zhen Li, Huazhong Ning, Liangliang Cao, Tong Zhang, Yihong Gong, Thomas S. Huang
• A Non-Parametric Approach to Dynamic Programming Oliver B. Kroemer, Jan R. Peters
• Advice Refinement in Knowledge-Based SVMs Gautam Kunapuli, Richard Maclin, Jude W. Shavlik
• Kernel Bayes' Rule Kenji Fukumizu, Le Song, Arthur Gretton
• Transfer from Multiple MDPs Alessandro Lazaric, Marcello Restelli
• Sparse Bayesian Multi-Task Learning Shengbo Guo, Onno Zoeter, Cédric Archambeau
• Online Learning: Stochastic, Constrained, and Smoothed Adversaries Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
• Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint Kenji Fukumizu, Gert R. Lanckriet, Bharath K. Sriperumbudur
• Sparse Recovery with Brownian Sensing Alexandra Carpentier, Odalric-ambrym Maillard, Rémi Munos
• An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments Michael C. Mozer, Benjamin Link, Harold Pashler
• Bayesian Bias Mitigation for Crowdsourcing Fabian L. Wauthier, Michael I. Jordan
• Ranking annotators for crowdsourced labeling tasks Vikas C. Raykar, Shipeng Yu
• Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery Scott Niekum, Andrew G. Barto
• Probabilistic Joint Image Segmentation and Labeling Adrian Ion, Joao Carreira, Cristian Sminchisescu
• Variance Reduction in Monte-Carlo Tree Search Joel Veness, Marc Lanctot, Michael Bowling
• Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors Chun-Nam Yu, Russell Greiner, Hsiu-Chin Lin, Vickie Baracos
• An Application of Tree-Structured Expectation Propagation for Channel Decoding Pablo M. Olmos, Luis Salamanca, Juan Fuentes, Fernando Pérez-Cruz
• High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions Animashree Anandkumar, Vincent Tan, Alan S. Willsky
• Structural equations and divisive normalization for energy-dependent component analysis Jun-ichiro Hirayama, Aapo Hyvärinen
• Robust Lasso with missing and grossly corrupted observations Nasser M. Nasrabadi, Trac D. Tran, Nam Nguyen
• A concave regularization technique for sparse mixture models Martin O. Larsson, Johan Ugander
• Learning a Distance Metric from a Network Blake Shaw, Bert Huang, Tony Jebara
• Variance Penalizing AdaBoost Pannagadatta K. Shivaswamy, Tony Jebara
• Efficient Offline Communication Policies for Factored Multiagent POMDPs João V. Messias, Matthijs Spaan, Pedro U. Lima
• Sparse recovery by thresholded non-negative least squares Martin Slawski, Matthias Hein
• On Learning Discrete Graphical Models using Greedy Methods Ali Jalali, Christopher C. Johnson, Pradeep K. Ravikumar
• Policy Gradient Coagent Networks Philip S. Thomas
• Iterative Learning for Reliable Crowdsourcing Systems David R. Karger, Sewoong Oh, Devavrat Shah
• A Model for Temporal Dependencies in Event Streams Asela Gunawardana, Christopher Meek, Puyang Xu
• Unsupervised learning models of primary cortical receptive fields and receptive field plasticity Maneesh Bhand, Ritvik Mudur, Bipin Suresh, Andrew Saxe, Andrew Y. Ng
• The Doubly Correlated Nonparametric Topic Model Dae I. Kim, Erik B. Sudderth
• MAP Inference for Bayesian Inverse Reinforcement Learning Jaedeug Choi, Kee-eung Kim
• Similarity-based Learning via Data Driven Embeddings Purushottam Kar, Prateek Jain
• Predicting Dynamic Difficulty Olana Missura, Thomas Gärtner
• Sparse Estimation with Structured Dictionaries David P. Wipf
• Spectral Methods for Learning Multivariate Latent Tree Structure Animashree Anandkumar, Kamalika Chaudhuri, Daniel J. Hsu, Sham M. Kakade, Le Song, Tong Zhang
• How biased are maximum entropy models? Jakob H. Macke, Iain Murray, Peter E. Latham
• Active learning of neural response functions with Gaussian processes Mijung Park, Greg Horwitz, Jonathan W. Pillow
• Priors over Recurrent Continuous Time Processes Ardavan Saeedi, Alexandre Bouchard-côté
• Learning to Learn with Compound HD Models Antonio Torralba, Joshua B. Tenenbaum, Ruslan R. Salakhutdinov
• Anatomically Constrained Decoding of Finger Flexion from Electrocorticographic Signals Zuoguan Wang, Gerwin Schalk, Qiang Ji
• Active Learning with a Drifting Distribution Liu Yang
• PiCoDes: Learning a Compact Code for Novel-Category Recognition Alessandro Bergamo, Lorenzo Torresani, Andrew W. Fitzgibbon
• Confidence Sets for Network Structure David S. Choi, Patrick J. Wolfe, Edo M. Airoldi
• Prismatic Algorithm for Discrete D.C. Programming Problem Yoshinobu Kawahara, Takashi Washio
• Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms Liefeng Bo, Xiaofeng Ren, Dieter Fox
• Multiclass Boosting: Theory and Algorithms Mohammad J. Saberian, Nuno Vasconcelos
• Learning with the weighted trace-norm under arbitrary sampling distributions Rina Foygel, Ohad Shamir, Nati Srebro, Ruslan R. Salakhutdinov
• Scalable Training of Mixture Models via Coresets Dan Feldman, Matthew Faulkner, Andreas Krause
• Generalised Coupled Tensor Factorisation Kenan Y. Yılmaz, Ali T. Cemgil, Umut Simsekli
• Nearest Neighbor based Greedy Coordinate Descent Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
• The Fixed Points of Off-Policy TD J. Z. Kolter
• Generalizing from Several Related Classification Tasks to a New Unlabeled Sample Gilles Blanchard, Gyemin Lee, Clayton Scott
• Trace Lasso: a trace norm regularization for correlated designs Edouard Grave, Guillaume R. Obozinski, Francis R. Bach
• Statistical Tests for Optimization Efficiency Levi Boyles, Anoop Korattikara, Deva Ramanan, Max Welling
• Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss Joseph Keshet, David A. McAllester
• A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm Julie Dethier, Paul Nuyujukian, Chris Eliasmith, Terrence C. Stewart, Shauki A. Elasaad, Krishna V. Shenoy, Kwabena A. Boahen
• Multi-Bandit Best Arm Identification Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric, Sébastien Bubeck
• Randomized Algorithms for Comparison-based Search Dominique Tschopp, Suhas Diggavi, Payam Delgosha, Soheil Mohajer
• Active Ranking using Pairwise Comparisons Kevin G. Jamieson, Robert Nowak
• An Empirical Evaluation of Thompson Sampling Olivier Chapelle, Lihong Li
• Blending Autonomous Exploration and Apprenticeship Learning Thomas J. Walsh, Daniel K. Hewlett, Clayton T. Morrison
• Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation Onur Dikmen, Cédric Févotte
• Evaluating the inverse decision-making approach to preference learning Alan Jern, Christopher G. Lucas, Charles Kemp
• Sparse Features for PCA-Like Linear Regression Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail
• The Manifold Tangent Classifier Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, Xavier Muller
• Analytical Results for the Error in Filtering of Gaussian Processes Alex K. Susemihl, Ron Meir, Manfred Opper
• Improved Algorithms for Linear Stochastic Bandits Yasin Abbasi-yadkori, Dávid Pál, Csaba Szepesvári
• Testing a Bayesian Measure of Representativeness Using a Large Image Database Joshua T. Abbott, Katherine A. Heller, Zoubin Ghahramani, Thomas L. Griffiths
• Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation Cho-jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Mátyás A. Sustik
• Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning Michalis K. Titsias, Miguel Lázaro-Gredilla
• Practical Variational Inference for Neural Networks Alex Graves
• Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability David P. Reichert, Peggy Series, Amos J. Storkey
• Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts Matthias Hein, Simon Setzer
• Fast and Accurate k-means For Large Datasets Michael Shindler, Alex Wong, Adam W. Meyerson
• A rational model of causal inference with continuous causes Thomas L. Griffiths, Michael James
• Quasi-Newton Methods for Markov Chain Monte Carlo Yichuan Zhang, Charles A. Sutton
• TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning George Konidaris, Scott Niekum, Philip S. Thomas
• Speedy Q-Learning Mohammad Ghavamzadeh, Hilbert J. Kappen, Mohammad G. Azar, Rémi Munos
• Regularized Laplacian Estimation and Fast Eigenvector Approximation Patrick O. Perry, Michael W. Mahoney
• Understanding the Intrinsic Memorability of Images Phillip Isola, Devi Parikh, Antonio Torralba, Aude Oliva
• The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning Marius Kloft, Gilles Blanchard
• Contextual Gaussian Process Bandit Optimization Andreas Krause, Cheng S. Ong
• Co-Training for Domain Adaptation Minmin Chen, Kilian Q. Weinberger, John Blitzer
• Autonomous Learning of Action Models for Planning Neville Mehta, Prasad Tadepalli, Alan Fern
• Gaussian process modulated renewal processes Yee W. Teh, Vinayak Rao
• Linear Submodular Bandits and their Application to Diversified Retrieval Yisong Yue, Carlos Guestrin
• Continuous-Time Regression Models for Longitudinal Networks Duy Q. Vu, David Hunter, Padhraic Smyth, Arthur U. Asuncion
• On Tracking The Partition Function Guillaume Desjardins, Yoshua Bengio, Aaron C. Courville
• Variational Gaussian Process Dynamical Systems Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence
• Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels Vikas Sindhwani, Aurelie C. Lozano
• Selecting Receptive Fields in Deep Networks Adam Coates, Andrew Y. Ng
• Convergent Fitted Value Iteration with Linear Function Approximation Daniel J. Lizotte
• Algorithms for Hyper-Parameter Optimization James S. Bergstra, Rémi Bardenet, Yoshua Bengio, Balázs Kégl
• Neural Reconstruction with Approximate Message Passing (NeuRAMP) Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, Aniruddha Bhargava
• Query-Aware MCMC Michael L. Wick, Andrew McCallum
• A reinterpretation of the policy oscillation phenomenon in approximate policy iteration Paul Wagner
• Inferring spike-timing-dependent plasticity from spike train data Ian Stevenson, Konrad Koerding
• Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints Omar Z. Khan, Pascal Poupart, John-mark M. Agosta
• A Collaborative Mechanism for Crowdsourcing Prediction Problems Jacob D. Abernethy, Rafael M. Frongillo
• Hierarchically Supervised Latent Dirichlet Allocation Adler J. Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett
• Select and Sample - A Model of Efficient Neural Inference and Learning Jacquelyn A. Shelton, Abdul S. Sheikh, Pietro Berkes, Joerg Bornschein, Joerg Luecke
• Selecting the State-Representation in Reinforcement Learning Odalric-ambrym Maillard, Daniil Ryabko, Rémi Munos
• Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning Joni K. Pajarinen, Jaakko Peltonen
• On the Universality of Online Mirror Descent Nati Srebro, Karthik Sridharan, Ambuj Tewari
• Demixed Principal Component Analysis Wieland Brendel, Ranulfo Romo, Christian K. Machens
• EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning Feng Yan, Yuan Qi
• Hashing Algorithms for Large-Scale Learning Ping Li, Anshumali Shrivastava, Joshua L. Moore, Arnd C. König
• Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound Iasonas Kokkinos
• Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation Nico Goernitz, Christian Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, Sören Sonnenburg
• Predicting response time and error rates in visual search Bo Chen, Vidhya Navalpakkam, Pietro Perona
• Kernel Embeddings of Latent Tree Graphical Models Le Song, Eric P. Xing, Ankur P. Parikh
• Inference in continuous-time change-point models Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor
• High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity Po-ling Loh, Martin J. Wainwright
• Exploiting spatial overlap to efficiently compute appearance distances between image windows Bogdan Alexe, Viviana Petrescu, Vittorio Ferrari

• Accelerated Adaptive Markov Chain for Partition Function Computation Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman

Advances in Neural Information Processing Systems 25 (NIPS 2012)

The papers below appear in Advances in Neural Information Processing Systems 25 edited by  F. Pereira  and  C.J.C. Burges  and  L. Bottou  and  K.Q. Weinberger .
They are proceedings from the conference, "Neural Information Processing Systems 2012."

• Locally Uniform Comparison Image Descriptor Andrew Ziegler, Eric Christiansen, David Kriegman, Serge J. Belongie
• Learning from Distributions via Support Measure Machines Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo, Prof. Bernhard Schölkopf
• Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery Ehsan Elhamifar, Guillermo Sapiro, René Vidal
• Feature Clustering for Accelerating Parallel Coordinate Descent Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin
• Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA C. M. Niu, Sirish Nandyala, Won J. Sohn, Terence Sanger
• Active Learning of Model Evidence Using Bayesian Quadrature Michael Osborne, Roman Garnett, Zoubin Ghahramani, David K. Duvenaud, Stephen J. Roberts, Carl E. Rasmussen
• Coupling Nonparametric Mixtures via Latent Dirichlet Processes Dahua Lin, John W. Fisher
• Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction Minjie Xu, Jun Zhu, Bo Zhang
• Bayesian Hierarchical Reinforcement Learning Feng Cao, Soumya Ray
• Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction Christoph H. Lampert
• Local Supervised Learning through Space Partitioning Joseph Wang, Venkatesh Saligrama
• A Generative Model for Parts-based Object Segmentation S. Eslami, Christopher Williams
• Super-Bit Locality-Sensitive Hashing Jianqiu Ji, Jianmin Li, Shuicheng Yan, Bo Zhang, Qi Tian
• The Bethe Partition Function of Log-supermodular Graphical Models Nicholas Ruozzi
• Random Utility Theory for Social Choice Hossein Azari, David Parks, Lirong Xia
• Putting Bayes to sleep Dmitry Adamskiy, Manfred K. Warmuth, Wouter M. Koolen
• A new metric on the manifold of kernel matrices with application to matrix geometric means Suvrit Sra
• Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification Wei Bi, James T. Kwok
• Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation Tuo Zhao, Kathryn Roeder, Han Liu
• Semiparametric Principal Component Analysis Fang Han, Han Liu
• Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing Shinsuke Koyama
• The representer theorem for Hilbert spaces: a necessary and sufficient condition Francesco Dinuzzo, Prof. Bernhard Schölkopf
• On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking Clément Calauzènes, Nicolas Usunier, Patrick Gallinari
• Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress Manuel Lopes, Tobias Lang, Marc Toussaint, Pierre-yves Oudeyer
• Supervised Learning with Similarity Functions Purushottam Kar, Prateek Jain
• Cocktail Party Processing via Structured Prediction Yuxuan Wang, Deliang Wang
• Robustness and risk-sensitivity in Markov decision processes Takayuki Osogami
• Dynamical And-Or Graph Learning for Object Shape Modeling and Detection Xiaolong Wang, Liang Lin
• Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions Alexandra Carpentier, Rémi Munos
• Distributed Non-Stochastic Experts Varun Kanade, Zhenming Liu, Bozidar Radunovic
• Learning Image Descriptors with the Boosting-Trick Tomasz Trzcinski, Mario Christoudias, Vincent Lepetit, Pascal Fua
• Fast Resampling Weighted v-Statistics Chunxiao Zhou, Jiseong Park, Yun Fu
• Multi-task Vector Field Learning Binbin Lin, Sen Yang, Chiyuan Zhang, Jieping Ye, Xiaofei He
• Memorability of Image Regions Aditya Khosla, Jianxiong Xiao, Antonio Torralba, Aude Oliva
• Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions Jaedeug Choi, Kee-eung Kim
• Automatic Feature Induction for Stagewise Collaborative Filtering Joonseok Lee, Mingxuan Sun, Seungyeon Kim, Guy Lebanon
• Selective Labeling via Error Bound Minimization Quanquan Gu, Tong Zhang, Jiawei Han, Chris H. Ding
• Volume Regularization for Binary Classification Koby Crammer, Tal Wagner
• Image Denoising and Inpainting with Deep Neural Networks Junyuan Xie, Linli Xu, Enhong Chen
• Max-Margin Structured Output Regression for Spatio-Temporal Action Localization Du Tran, Junsong Yuan
• Transelliptical Component Analysis Fang Han, Han Liu
• Action-Model Based Multi-agent Plan Recognition Hankz H. Zhuo, Qiang Yang, Subbarao Kambhampati
• Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity Angela Eigenstetter, Bjorn Ommer
• Non-parametric Approximate Dynamic Programming via the Kernel Method Nikhil Bhat, Vivek Farias, Ciamac C. Moallemi
• Optimal Regularized Dual Averaging Methods for Stochastic Optimization Xi Chen, Qihang Lin, Javier Pena
• The variational hierarchical EM algorithm for clustering hidden Markov models Emanuele Coviello, Gert R. Lanckriet, Antoni B. Chan
• Truncation-free Online Variational Inference for Bayesian Nonparametric Models Chong Wang, David M. Blei
• 3D Social Saliency from Head-mounted Cameras Hyun S. Park, Eakta Jain, Yaser Sheikh
• Context-Sensitive Decision Forests for Object Detection Peter Kontschieder, Samuel R. Bulò, Antonio Criminisi, Pushmeet Kohli, Marcello Pelillo, Horst Bischof
• Learning Invariant Representations of Molecules for Atomization Energy Prediction Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, Anatole V. Lilienfeld, Klaus-Robert Müller
• Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button Joan Fruitet, Alexandra Carpentier, Maureen Clerc, Rémi Munos
• Multiplicative Forests for Continuous-Time Processes Jeremy Weiss, Sriraam Natarajan, David Page
• Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task Jenna Wiens, Eric Horvitz, John V. Guttag
• Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison Tianbao Yang, Yu-feng Li, Mehrdad Mahdavi, Rong Jin, Zhi-Hua Zhou
• Multiclass Learning Approaches: A Theoretical Comparison with Implications Amit Daniely, Sivan Sabato, Shai S. Shwartz
• Stochastic Gradient Descent with Only One Projection Mehrdad Mahdavi, Tianbao Yang, Rong Jin, Shenghuo Zhu, Jinfeng Yi
• Neuronal Spike Generation Mechanism as an Oversampling, Noise-shaping A-to-D converter Dmitri B. Chklovskii, Daniel Soudry
• Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction Pietro D. Lena, Ken Nagata, Pierre F. Baldi
• Assessing Blinding in Clinical Trials Ognjen Arandjelovic
• Scalable nonconvex inexact proximal splitting Suvrit Sra
• Learning to Discover Social Circles in Ego Networks Jure Leskovec, Julian J. Mcauley
• A Conditional Multinomial Mixture Model for Superset Label Learning Liping Liu, Thomas G. Dietterich
• Majorization for CRFs and Latent Likelihoods Tony Jebara, Anna Choromanska
• Ensemble weighted kernel estimators for multivariate entropy estimation Kumar Sricharan, Alfred O. Hero
• Efficient high dimensional maximum entropy modeling via symmetric partition functions Paul Vernaza, Drew Bagnell
• Discriminatively Trained Sparse Code Gradients for Contour Detection Ren Xiaofeng, Liefeng Bo
• Analyzing 3D Objects in Cluttered Images Mohsen Hejrati, Deva Ramanan
• Nonconvex Penalization Using Laplace Exponents and Concave Conjugates Zhihua Zhang, Bojun Tu
• 3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model Sanja Fidler, Sven Dickinson, Raquel Urtasun
• Structured Learning of Gaussian Graphical Models Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-in Lee, Maryam Fazel
• A Polylog Pivot Steps Simplex Algorithm for Classification Elad Hazan, Zohar Karnin
• Shifting Weights: Adapting Object Detectors from Image to Video Kevin Tang, Vignesh Ramanathan, Li Fei-fei, Daphne Koller
• A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound Shusen Wang, Zhihua Zhang
• Convolutional-Recursive Deep Learning for 3D Object Classification Richard Socher, Brody Huval, Bharath Bath, Christopher D. Manning, Andrew Y. Ng
• Semi-Supervised Domain Adaptation with Non-Parametric Copulas David Lopez-paz, Jose M. Hernández-lobato, Prof. Bernhard Schölkopf
• Identification of Recurrent Patterns in the Activation of Brain Networks Firdaus Janoos, Weichang Li, Niranjan Subrahmanya, Istvan Morocz, William Wells
• Density-Difference Estimation Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki, Marthinus D. Plessis, Song Liu, Ichiro Takeuchi
• Variational Inference for Crowdsourcing Qiang Liu, Jian Peng, Alexander T. Ihler
• MCMC for continuous-time discrete-state systems Vinayak Rao, Yee W. Teh
• A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling Pieter-jan Kindermans, Hannes Verschore, David Verstraeten, Benjamin Schrauwen
• Learning about Canonical Views from Internet Image Collections Elad Mezuman, Yair Weiss
• Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data Assaf Glazer, Michael Lindenbaum, Shaul Markovitch
• Multiresolution Gaussian Processes Emily Fox, David B. Dunson
• Localizing 3D cuboids in single-view images Jianxiong Xiao, Bryan Russell, Antonio Torralba
• Newton-Like Methods for Sparse Inverse Covariance Estimation Figen Oztoprak, Jorge Nocedal, Steven Rennie, Peder A. Olsen
• Learning to Align from Scratch Gary Huang, Marwan Mattar, Honglak Lee, Erik G. Learned-miller
• Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints Stefan Habenschuss, Johannes Bill, Bernhard Nessler
• Clustering Aggregation as Maximum-Weight Independent Set Nan Li, Longin J. Latecki
• Topology Constraints in Graphical Models Marcelo Fiori, Pablo Musé, Guillermo Sapiro
• Transelliptical Graphical Models Han Liu, Fang Han, Cun-hui Zhang
• Kernel Latent SVM for Visual Recognition Weilong Yang, Yang Wang, Arash Vahdat, Greg Mori
• Learning Partially Observable Models Using Temporally Abstract Decision Trees Erik Talvitie
• Proximal Newton-type methods for convex optimization Jason D. Lee, Yuekai Sun, Michael Saunders
• Regularized Off-Policy TD-Learning Bo Liu, Sridhar Mahadevan, Ji Liu
• Multi-criteria Anomaly Detection using Pareto Depth Analysis Ko-jen Hsiao, Kevin Xu, Jeff Calder, Alfred O. Hero
• Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes Jake Bouvrie, Jean-jeacques Slotine
• Calibrated Elastic Regularization in Matrix Completion Tingni Sun, Cun-hui Zhang
• Predicting Action Content On-Line and in Real Time before Action Onset – an Intracranial Human Study Uri Maoz, Shengxuan Ye, Ian Ross, Adam Mamelak, Christof Koch
• Searching for objects driven by context Bogdan Alexe, Nicolas Heess, Yee W. Teh, Vittorio Ferrari
• Timely Object Recognition Sergey Karayev, Tobias Baumgartner, Mario Fritz, Trevor Darrell
• Nonparanormal Belief Propagation (NPNBP) Gal Elidan, Cobi Cario
• Deep Representations and Codes for Image Auto-Annotation Ryan Kiros, Csaba Szepesvári
• A Spectral Algorithm for Latent Dirichlet Allocation Anima Anandkumar, Dean P. Foster, Daniel J. Hsu, Sham M. Kakade, Yi-kai Liu
• Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs Aharon Birnbaum, Shai S. Shwartz
• Matrix reconstruction with the local max norm Rina Foygel, Nathan Srebro, Ruslan R. Salakhutdinov
• Analog readout for optical reservoir computers Anteo Smerieri, François Duport, Yvon Paquot, Benjamin Schrauwen, Marc Haelterman, Serge Massar
• Accuracy at the Top Stephen Boyd, Corinna Cortes, Mehryar Mohri, Ana Radovanovic
• Minimizing Sparse High-Order Energies by Submodular Vertex-Cover Andrew Delong, Olga Veksler, Anton Osokin, Yuri Boykov
• Perfect Dimensionality Recovery by Variational Bayesian PCA Shinichi Nakajima, Ryota Tomioka, Masashi Sugiyama, S. D. Babacan
• Mirror Descent Meets Fixed Share (and feels no regret) Nicolò Cesa-bianchi, Pierre Gaillard, Gabor Lugosi, Gilles Stoltz
• Near-optimal Differentially Private Principal Components Kamalika Chaudhuri, Anand Sarwate, Kaushik Sinha
• Random function priors for exchangeable arrays with applications to graphs and relational data James Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy
• Inverse Reinforcement Learning through Structured Classification Edouard Klein, Matthieu Geist, Bilal Piot, Olivier Pietquin
• Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics Ashwini Shukla, Aude Billard
• Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search Arthur Guez, David Silver, Peter Dayan
• Dimensionality Dependent PAC-Bayes Margin Bound Chi Jin, Liwei Wang
• Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs Anima Anandkumar, Ragupathyraj Valluvan
• Learning Mixtures of Tree Graphical Models Anima Anandkumar, Daniel J. Hsu, Furong Huang, Sham M. Kakade
• Hamming Distance Metric Learning Mohammad Norouzi, David J. Fleet, Ruslan R. Salakhutdinov
• Spiking and saturating dendrites differentially expand single neuron computation capacity Romain Cazé, Mark Humphries, Boris S. Gutkin
• Clustering by Nonnegative Matrix Factorization Using Graph Random Walk Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, Erkki Oja
• Delay Compensation with Dynamical Synapses Chi Fung, K. Wong, Si Wu
• ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
• Recognizing Activities by Attribute Dynamics Weixin Li, Nuno Vasconcelos
• Compressive Sensing MRI with Wavelet Tree Sparsity Chen Chen, Junzhou Huang
• Training sparse natural image models with a fast Gibbs sampler of an extended state space Lucas Theis, Jascha Sohl-dickstein, Matthias Bethge
• A Bayesian Approach for Policy Learning from Trajectory Preference Queries Aaron Wilson, Alan Fern, Prasad Tadepalli
• GenDeR: A Generic Diversified Ranking Algorithm Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw Szymanski
• On Multilabel Classification and Ranking with Partial Feedback Claudio Gentile, Francesco Orabona
• The Lovász ϑ function, SVMs and finding large dense subgraphs Vinay Jethava, Anders Martinsson, Chiranjib Bhattacharyya, Devdatt Dubhashi
• Multi-Task Averaging Sergey Feldman, Maya Gupta, Bela Frigyik
• Unsupervised Structure Discovery for Semantic Analysis of Audio Sourish Chaudhuri, Bhiksha Raj
• A Marginalized Particle Gaussian Process Regression Yali Wang, Brahim Chaib-draa
• Angular Quantization-based Binary Codes for Fast Similarity Search Yunchao Gong, Sanjiv Kumar, Vishal Verma, Svetlana Lazebnik
• Optimal kernel choice for large-scale two-sample tests Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, Bharath K. Sriperumbudur
• Factoring nonnegative matrices with linear programs Ben Recht, Christopher Re, Joel Tropp, Victor Bittorf
• Large Scale Distributed Deep Networks Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc'aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, Quoc V. Le, Andrew Y. Ng
• Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Tie-yan Liu
• Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination Won H. Kim, Deepti Pachauri, Charles Hatt, Moo. K. Chung, Sterling Johnson, Vikas Singh
• A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation Aaron Defazio, Tibério S. Caetano
• Fused sparsity and robust estimation for linear models with unknown variance Arnak Dalalyan, Yin Chen
• How Prior Probability Influences Decision Making: A Unifying Probabilistic Model Yanping Huang, Timothy Hanks, Mike Shadlen, Abram L. Friesen, Rajesh P. Rao
• High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer's Disease Progression Prediction Hua Wang, Feiping Nie, Heng Huang, Jingwen Yan, Sungeun Kim, Shannon Risacher, Andrew Saykin, Li Shen
• Symmetric Correspondence Topic Models for Multilingual Text Analysis Kosuke Fukumasu, Koji Eguchi, Eric P. Xing
• Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data Michael C. Hughes, Emily Fox, Erik B. Sudderth
• Efficient coding provides a direct link between prior and likelihood in perceptual Bayesian inference Xue-xin Wei, Alan A. Stocker
• Efficient Sampling for Bipartite Matching Problems Maksims Volkovs, Richard S. Zemel
• Learning visual motion in recurrent neural networks Marius Pachitariu, Maneesh Sahani
• Learned Prioritization for Trading Off Accuracy and Speed Jiarong Jiang, Adam Teichert, Jason Eisner, Hal Daume
• Value Pursuit Iteration Amir M. Farahmand, Doina Precup
• Compressive neural representation of sparse, high-dimensional probabilities Xaq Pitkow
• Graphical Models via Generalized Linear Models Eunho Yang, Genevera Allen, Zhandong Liu, Pradeep K. Ravikumar
• CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem Henrik Ohlsson, Allen Yang, Roy Dong, Shankar Sastry
• Co-Regularized Hashing for Multimodal Data Yi Zhen, Dit-Yan Yeung
• Convergence and Energy Landscape for Cheeger Cut Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James V. Brecht
• Symbolic Dynamic Programming for Continuous State and Observation POMDPs Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting
• Bayesian Probabilistic Co-Subspace Addition Lei Shi
• Scaled Gradients on Grassmann Manifolds for Matrix Completion Thanh Ngo, Yousef Saad
• Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging Chris Hinrichs, Vikas Singh, Jiming Peng, Sterling Johnson
• Privacy Aware Learning Martin J. Wainwright, Michael I. Jordan, John C. Duchi
• Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods Andre Wibisono, Martin J. Wainwright, Michael I. Jordan, John C. Duchi
• Hierarchical Optimistic Region Selection driven by Curiosity Odalric-ambrym Maillard
• Sparse Prediction with the k-Support Norm Andreas Argyriou, Rina Foygel, Nathan Srebro
• Active Learning of Multi-Index Function Models Tyagi Hemant, Volkan Cevher
• Learning Multiple Tasks using Shared Hypotheses Koby Crammer, Yishay Mansour
• On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization Doina Precup, Joelle Pineau, Andre S. Barreto
• Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models Kei Wakabayashi, Takao Miura
• Communication-Efficient Algorithms for Statistical Optimization Yuchen Zhang, Martin J. Wainwright, John C. Duchi
• Identifiability and Unmixing of Latent Parse Trees Daniel J. Hsu, Sham M. Kakade, Percy S. Liang
• Bayesian nonparametric models for ranked data Francois Caron, Yee W. Teh
• Feature-aware Label Space Dimension Reduction for Multi-label Classification Yao-nan Chen, Hsuan-tien Lin
• Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions Alekh Agarwal, Sahand Negahban, Martin J. Wainwright
• Graphical Gaussian Vector for Image Categorization Tatsuya Harada, Yasuo Kuniyoshi
• Joint Modeling of a Matrix with Associated Text via Latent Binary Features Xianxing Zhang, Lawrence Carin
• Proper losses for learning from partial labels Jesús Cid-sueiro
• Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation Benjamin Rolfs, Bala Rajaratnam, Dominique Guillot, Ian Wong, Arian Maleki
• Selecting Diverse Features via Spectral Regularization Abhimanyu Das, Anirban Dasgupta, Ravi Kumar
• Monte Carlo Methods for Maximum Margin Supervised Topic Models Qixia Jiang, Jun Zhu, Maosong Sun, Eric P. Xing
• Parametric Local Metric Learning for Nearest Neighbor Classification Jun Wang, Alexandros Kalousis, Adam Woznica
• A Linear Time Active Learning Algorithm for Link Classification Nicolò Cesa-bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella
• Bayesian Warped Gaussian Processes Miguel Lázaro-Gredilla
• Nonparametric Reduced Rank Regression Rina Foygel, Michael Horrell, Mathias Drton, John D. Lafferty
• Multiresolution analysis on the symmetric group Risi Kondor, Walter Dempsey
• Isotropic Hashing Weihao Kong, Wu-jun Li
• On Lifting the Gibbs Sampling Algorithm Deepak Venugopal, Vibhav Gogate
• On the connections between saliency and tracking Vijay Mahadevan, Nuno Vasconcelos
• Convex Multi-view Subspace Learning Martha White, Xinhua Zhang, Dale Schuurmans, Yao-liang Yu
• Spectral learning of linear dynamics from generalised-linear observations with application to neural population data Lars Buesing, Jakob H. Macke, Maneesh Sahani
• Mixability in Statistical Learning Tim V. Erven, Peter Grünwald, Mark D. Reid, Robert C. Williamson
• Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation Christian Mayr, Paul Stärke, Johannes Partzsch, Love Cederstroem, Rene Schüffny, Yao Shuai, Nan Du, Heidemarie Schmidt
• A lattice filter model of the visual pathway Karol Gregor, Dmitri B. Chklovskii
• Semantic Kernel Forests from Multiple Taxonomies Sung Ju Hwang, Kristen Grauman, Fei Sha
• Causal discovery with scale-mixture model for spatiotemporal variance dependencies Zhitang Chen, Kun Zhang, Laiwan Chan
• Natural Images, Gaussian Mixtures and Dead Leaves Daniel Zoran, Yair Weiss
• Dual-Space Analysis of the Sparse Linear Model Yi Wu, David P. Wipf
• Active Comparison of Prediction Models Christoph Sawade, Niels Landwehr, Tobias Scheffer
• Online Regret Bounds for Undiscounted Continuous Reinforcement Learning Ronald Ortner, Daniil Ryabko
• Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning Jinfeng Yi, Rong Jin, Shaili Jain, Tianbao Yang, Anil K. Jain
• Learning curves for multi-task Gaussian process regression Peter Sollich, Simon Ashton
• Kernel Hyperalignment Alexander Lorbert, Peter J. Ramadge
• Multiple Choice Learning: Learning to Produce Multiple Structured Outputs Abner Guzmán-rivera, Dhruv Batra, Pushmeet Kohli
• Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions Mathieu Sinn, Bei Chen
• Persistent Homology for Learning Densities with Bounded Support Florian T. Pokorny, Hedvig Kjellström, Danica Kragic, Carl Ek
• On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes Bruno Scherrer, Boris Lesner
• Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model Sander M. Bohte
• MAP Inference in Chains using Column Generation David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum
• Bayesian Nonparametric Modeling of Suicide Attempts Francisco Ruiz, Isabel Valera, Carlos Blanco, Fernando Pérez-Cruz
• Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling Antonino Freno, Mikaela Keller, Marc Tommasi
• Neurally Plausible Reinforcement Learning of Working Memory Tasks Jaldert Rombouts, Pieter Roelfsema, Sander M. Bohte
• Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions Neil Burch, Marc Lanctot, Duane Szafron, Richard G. Gibson
• Repulsive Mixtures Francesca Petralia, Vinayak Rao, David B. Dunson
• Fully Bayesian inference for neural models with negative-binomial spiking James Scott, Jonathan W. Pillow
• Slice Normalized Dynamic Markov Logic Networks Tivadar Papai, Henry Kautz, Daniel Stefankovic
• Meta-Gaussian Information Bottleneck Melanie Rey, Volker Roth
• Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification Yung-kyun Noh, Frank Park, Daniel D. Lee
• The Perturbed Variation Maayan Harel, Shie Mannor
• Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization Konstantinos Tsianos, Sean Lawlor, Michael G. Rabbat
• The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes Simon Lyons, Amos J. Storkey, Simo Särkkä
• Online allocation and homogeneous partitioning for piecewise constant mean-approximation Alexandra Carpentier, Odalric-ambrym Maillard
• Learning as MAP Inference in Discrete Graphical Models Xianghang Liu, James Petterson, Tibério S. Caetano
• A mechanistic model of early sensory processing based on subtracting sparse representations Shaul Druckmann, Tao Hu, Dmitri B. Chklovskii
• Multi-Stage Multi-Task Feature Learning Pinghua Gong, Jieping Ye, Chang-shui Zhang
• From Deformations to Parts: Motion-based Segmentation of 3D Objects Soumya Ghosh, Matthew Loper, Erik B. Sudderth, Michael J. Black
• Phoneme Classification using Constrained Variational Gaussian Process Dynamical System Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang D. Yoo
• Bayesian estimation of discrete entropy with mixtures of stick-breaking priors Evan Archer, Il Memming Park, Jonathan W. Pillow
• A Geometric take on Metric Learning Søren Hauberg, Oren Freifeld, Michael J. Black
• Learning the Architecture of Sum-Product Networks Using Clustering on Variables Aaron Dennis, Dan Ventura
• Pointwise Tracking the Optimal Regression Function Yair Wiener, Ran El-Yaniv
• Bayesian nonparametric models for bipartite graphs Francois Caron
• Reducing statistical time-series problems to binary classification Daniil Ryabko, Jeremie Mary
• Tractable Objectives for Robust Policy Optimization Katherine Chen, Michael Bowling
• Classification Calibration Dimension for General Multiclass Losses Harish G. Ramaswamy, Shivani Agarwal
• Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses Po-ling Loh, Martin J. Wainwright
• Collaborative Gaussian Processes for Preference Learning Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Jose M. Hernández-lobato
• Approximating Concavely Parameterized Optimization Problems Joachim Giesen, Jens Mueller, Soeren Laue, Sascha Swiercy
• Gradient-based kernel method for feature extraction and variable selection Kenji Fukumizu, Chenlei Leng
• Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making Pradeep Shenoy, Angela J. Yu
• On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks Qirong Ho, Junming Yin, Eric P. Xing
• Relax and Randomize : From Value to Algorithms Sasha Rakhlin, Ohad Shamir, Karthik Sridharan
• Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL Nishant Mehta, Dongryeol Lee, Alexander G. Gray
• Spectral Learning of General Weighted Automata via Constrained Matrix Completion Borja Balle, Mehryar Mohri
• Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum L_p Loss Zhuo Wang, Alan A. Stocker, Daniel D. Lee
• Algorithms for Learning Markov Field Policies Abdeslam Boularias, Jan R. Peters, Oliver B. Kroemer
• Affine Independent Variational Inference Edward Challis, David Barber
• Learning from the Wisdom of Crowds by Minimax Entropy Denny Zhou, Sumit Basu, Yi Mao, John C. Platt
• Clustering Sparse Graphs Yudong Chen, Sujay Sanghavi, Huan Xu
• Sketch-Based Linear Value Function Approximation Marc Bellemare, Joel Veness, Michael Bowling
• Multimodal Learning with Deep Boltzmann Machines Nitish Srivastava, Ruslan R. Salakhutdinov
• Learning with Target Prior Zuoguan Wang, Siwei Lyu, Gerwin Schalk, Qiang Ji
• Slice sampling normalized kernel-weighted completely random measure mixture models Nicholas Foti, Sinead Williamson
• Scalable Inference of Overlapping Communities Prem K. Gopalan, Sean Gerrish, Michael Freedman, David M. Blei, David M. Mimno
• Online L1-Dictionary Learning with Application to Novel Document Detection Shiva P. Kasiviswanathan, Huahua Wang, Arindam Banerjee, Prem Melville
• A systematic approach to extracting semantic information from functional MRI data Francisco Pereira, Matthew Botvinick
• Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding Philip Sterne, Joerg Bornschein, Abdul-saboor Sheikh, Joerg Luecke, Jacquelyn A. Shelton
• Learning optimal spike-based representations Ralph Bourdoukan, David Barrett, Sophie Deneve, Christian K. Machens
• Collaborative Ranking With 17 Parameters Maksims Volkovs, Richard S. Zemel
• Rational inference of relative preferences Nisheeth Srivastava, Paul R. Schrater
• The topographic unsupervised learning of natural sounds in the auditory cortex Hiroki Terashima, Masato Okada
• Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand Amy Greenwald, Jiacui Li, Eric Sodomka
• A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation Cho-jui Hsieh, Arindam Banerjee, Inderjit S. Dhillon, Pradeep K. Ravikumar
• A Simple and Practical Algorithm for Differentially Private Data Release Moritz Hardt, Katrina Ligett, Frank Mcsherry
• Bayesian active learning with localized priors for fast receptive field characterization Mijung Park, Jonathan W. Pillow
• Weighted Likelihood Policy Search with Model Selection Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara
• Learning the Dependency Structure of Latent Factors Yunlong He, Yanjun Qi, Koray Kavukcuoglu, Haesun Park
• Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders Sanjeev Arora, Rong Ge, Ankur Moitra, Sushant Sachdeva
• Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins Alex Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun
• Dip-means: an incremental clustering method for estimating the number of clusters Argyris Kalogeratos, Aristidis Likas
• No-Regret Algorithms for Unconstrained Online Convex Optimization Brendan Mcmahan, Matthew Streeter
• Bayesian models for Large-scale Hierarchical Classification Siddharth Gopal, Yiming Yang, Bing Bai, Alexandru Niculescu-mizil
• Recovery of Sparse Probability Measures via Convex Programming Mert Pilanci, Laurent E. Ghaoui, Venkat Chandrasekaran
• Multiple Operator-valued Kernel Learning Hachem Kadri, Alain Rakotomamonjy, Philippe Preux, Francis R. Bach
• Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning Ulugbek Kamilov, Sundeep Rangan, Michael Unser, Alyson K. Fletcher
• A Better Way to Pretrain Deep Boltzmann Machines Geoffrey E. Hinton, Ruslan R. Salakhutdinov
• Towards a learning-theoretic analysis of spike-timing dependent plasticity David Balduzzi, Michel Besserve
• Learning Manifolds with K-Means and K-Flats Guillermo Canas, Tomaso Poggio, Lorenzo Rosasco
• Iterative ranking from pair-wise comparisons Sahand Negahban, Sewoong Oh, Devavrat Shah
• A Polynomial-time Form of Robust Regression Yao-liang Yu, Özlem Aslan, Dale Schuurmans
• Learning Probability Measures with respect to Optimal Transport Metrics Guillermo Canas, Lorenzo Rosasco
• Label Ranking with Partial Abstention based on Thresholded Probabilistic Models Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker
• Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting Amadou Ba, Mathieu Sinn, Yannig Goude, Pascal Pompey
• Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs Michael Collins, Shay B. Cohen
• Semi-supervised Eigenvectors for Locally-biased Learning Toke Hansen, Michael W. Mahoney
• Exponential Concentration for Mutual Information Estimation with Application to Forests Han Liu, Larry Wasserman, John D. Lafferty
• Augment-and-Conquer Negative Binomial Processes Mingyuan Zhou, Lawrence Carin
• Transferring Expectations in Model-based Reinforcement Learning Trung Nguyen, Tomi Silander, Tze Y. Leong
• Minimization of Continuous Bethe Approximations: A Positive Variation Jason Pacheco, Erik B. Sudderth
• Non-linear Metric Learning Dor Kedem, Stephen Tyree, Fei Sha, Gert R. Lanckriet, Kilian Q. Weinberger
• Factorial LDA: Sparse Multi-Dimensional Text Models Michael Paul, Mark Dredze
• Ancestor Sampling for Particle Gibbs Fredrik Lindsten, Thomas Schön, Michael I. Jordan
• Modelling Reciprocating Relationships with Hawkes Processes Charles Blundell, Jeff Beck, Katherine A. Heller
• Expectation Propagation in Gaussian Process Dynamical Systems Marc Deisenroth, Shakir Mohamed
• A quasi-Newton proximal splitting method Stephen Becker, Jalal Fadili
• Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization Demba Ba, Behtash Babadi, Patrick Purdon, Emery Brown
• Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems Morteza Ibrahimi, Adel Javanmard, Benjamin V. Roy
• Multilabel Classification using Bayesian Compressed Sensing Ashish Kapoor, Raajay Viswanathan, Prateek Jain
• Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization Stephen Bach, Matthias Broecheler, Lise Getoor, Dianne O'leary
• A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets Nicolas L. Roux, Mark Schmidt, Francis R. Bach
• Query Complexity of Derivative-Free Optimization Kevin G. Jamieson, Robert Nowak, Ben Recht
• Emergence of Object-Selective Features in Unsupervised Feature Learning Adam Coates, Andrej Karpathy, Andrew Y. Ng
• Burn-in, bias, and the rationality of anchoring Falk Lieder, Thomas Griffiths, Noah Goodman
• Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes Michael Bryant, Erik B. Sudderth
• A Neural Autoregressive Topic Model Hugo Larochelle, Stanislas Lauly
• A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes Thomas Furmston, David Barber
• Entangled Monte Carlo Seong-hwan Jun, Liangliang Wang, Alexandre Bouchard-côté
• Near-Optimal MAP Inference for Determinantal Point Processes Jennifer Gillenwater, Alex Kulesza, Ben Taskar
• Probabilistic Low-Rank Subspace Clustering S. D. Babacan, Shinichi Nakajima, Minh Do
• How They Vote: Issue-Adjusted Models of Legislative Behavior Sean Gerrish, David M. Blei
• Density Propagation and Improved Bounds on the Partition Function Stefano Ermon, Ashish Sabharwal, Bart Selman, Carla P. Gomes
• Perceptron Learning of SAT Alex Flint, Matthew Blaschko
• Learning Networks of Heterogeneous Influence Nan Du, Le Song, Ming Yuan, Alex J. Smola
• Multiclass Learning with Simplex Coding Youssef Mroueh, Tomaso Poggio, Lorenzo Rosasco, Jean-jeacques Slotine
• FastEx: Hash Clustering with Exponential Families Amr Ahmed, Sujith Ravi, Alex J. Smola, Shravan M. Narayanamurthy
• Topic-Partitioned Multinetwork Embeddings Peter Krafft, Juston Moore, Bruce Desmarais, Hanna M. Wallach
• Learning Label Trees for Probabilistic Modelling of Implicit Feedback Andriy Mnih, Yee W. Teh
• Learning with Recursive Perceptual Representations Oriol Vinyals, Yangqing Jia, Li Deng, Trevor Darrell
• Link Prediction in Graphs with Autoregressive Features Emile Richard, Stephane Gaiffas, Nicolas Vayatis
• Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images Dan Ciresan, Alessandro Giusti, Luca M. Gambardella, Juergen Schmidhuber
• Scalable imputation of genetic data with a discrete fragmentation-coagulation process Lloyd Elliott, Yee W. Teh
• Gradient Weights help Nonparametric Regressors Samory Kpotufe, Abdeslam Boularias
• Online Sum-Product Computation Over Trees Mark Herbster, Stephen Pasteris, Fabio Vitale
• Sparse Approximate Manifolds for Differential Geometric MCMC Ben Calderhead, Mátyás A. Sustik
• Fast Variational Inference in the Conjugate Exponential Family James Hensman, Magnus Rattray, Neil D. Lawrence
• Bayesian Pedigree Analysis using Measure Factorization Bonnie Kirkpatrick, Alexandre Bouchard-côté
• Accelerated Training for Matrix-norm Regularization: A Boosting Approach Xinhua Zhang, Dale Schuurmans, Yao-liang Yu
• Controlled Recognition Bounds for Visual Learning and Exploration Vasiliy Karasev, Alessandro Chiuso, Stefano Soatto
• Distributed Probabilistic Learning for Camera Networks with Missing Data Sejong Yoon, Vladimir Pavlovic
• Submodular-Bregman and the Lovász-Bregman Divergences with Applications Rishabh Iyer, Jeff A. Bilmes
• Minimizing Uncertainty in Pipelines Nilesh Dalvi, Aditya Parameswaran, Vibhor Rastogi
• Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan P. Adams
• Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach Dijun Luo, Heng Huang, Feiping Nie, Chris H. Ding
• The Time-Marginalized Coalescent Prior for Hierarchical Clustering Levi Boyles, Max Welling
• Fusion with Diffusion for Robust Visual Tracking Yu Zhou, Xiang Bai, Wenyu Liu, Longin J. Latecki
• A nonparametric variable clustering model Konstantina Palla, Zoubin Ghahramani, David A. Knowles
• Priors for Diversity in Generative Latent Variable Models James T. Kwok, Ryan P. Adams
• A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function Pedro Ortega, Jordi Grau-moya, Tim Genewein, David Balduzzi, Daniel Braun
• Convergence Rate Analysis of MAP Coordinate Minimization Algorithms Ofer Meshi, Amir Globerson, Tommi S. Jaakkola
• Projection Retrieval for Classification Madalina Fiterau, Artur Dubrawski
• Hierarchical spike coding of sound Yan Karklin, Chaitanya Ekanadham, Eero P. Simoncelli
• Human memory search as a random walk in a semantic network Joseph L. Austerweil, Joshua T. Abbott, Thomas L. Griffiths
• Probabilistic n-Choose-k Models for Classification and Ranking Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel, Ryan P. Adams
• Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models Jeff Beck, Alexandre Pouget, Katherine A. Heller
• Cost-Sensitive Exploration in Bayesian Reinforcement Learning Dongho Kim, Kee-eung Kim, Pascal Poupart
• Learning with Partially Absorbing Random Walks Xiao-ming Wu, Zhenguo Li, Anthony M. So, John Wright, Shih-fu Chang
• Locating Changes in Highly Dependent Data with Unknown Number of Change Points Azadeh Khaleghi, Daniil Ryabko
• Probabilistic Event Cascades for Alzheimer's disease Jonathan Huang, Daniel Alexander
• Efficient and direct estimation of a neural subunit model for sensory coding Brett Vintch, Andrew Zaharia, J Movshon, Eero P. Simoncelli
• One Permutation Hashing Ping Li, Art Owen, Cun-hui Zhang
• Unsupervised Template Learning for Fine-Grained Object Recognition Shulin Yang, Liefeng Bo, Jue Wang, Linda G. Shapiro
• Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds Teodor M. Moldovan, Pieter Abbeel
• Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression Emtiyaz Khan, Shakir Mohamed, Kevin P. Murphy
• Imitation Learning by Coaching He He, Jason Eisner, Hal Daume
• Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models Ke Jiang, Brian Kulis, Michael I. Jordan
• A latent factor model for highly multi-relational data Rodolphe Jenatton, Nicolas L. Roux, Antoine Bordes, Guillaume R. Obozinski
• Entropy Estimations Using Correlated Symmetric Stable Random Projections Ping Li, Cun-hui Zhang
• Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression Piyush Rai, Abhishek Kumar, Hal Daume
• Continuous Relaxations for Discrete Hamiltonian Monte Carlo Yichuan Zhang, Zoubin Ghahramani, Amos J. Storkey, Charles A. Sutton
• Deep Learning of Invariant Features via Simulated Fixations in Video Will Zou, Shenghuo Zhu, Kai Yu, Andrew Y. Ng
• Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence Victor Gabillon, Mohammad Ghavamzadeh, Alessandro Lazaric
• On the Sample Complexity of Robust PCA Matthew Coudron, Gilad Lerman
• Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning Matthew Der, Lawrence K. Saul
• Discriminative Learning of Sum-Product Networks Robert Gens, Pedro Domingos
• Trajectory-Based Short-Sighted Probabilistic Planning Felipe Trevizan, Manuela Veloso
• Tight Bounds on Profile Redundancy and Distinguishability Jayadev Acharya, Hirakendu Das, Alon Orlitsky
• Interpreting prediction markets: a stochastic approach Rafael M. Frongillo, Nicholás Della Penna, Mark D. Reid
• Risk-Aversion in Multi-armed Bandits Amir Sani, Alessandro Lazaric, Rémi Munos
• Confusion-Based Online Learning and a Passive-Aggressive Scheme Liva Ralaivola
• Cardinality Restricted Boltzmann Machines Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan R. Salakhutdinov, Ryan P. Adams
• Generalization Bounds for Domain Adaptation Chao Zhang, Lei Zhang, Jieping Ye

Advances in Neural Information Processing Systems 26 (NIPS 2013)

The papers below appear in Advances in Neural Information Processing Systems 26 edited by  C.J.C. Burges  and  L. Bottou  and  M. Welling  and  Z. Ghahramani  and  K.Q. Weinberger .
They are proceedings from the conference, "Neural Information Processing Systems 2013."

• The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Prof. Bernhard Schölkopf
• Documents as multiple overlapping windows into grids of counts Alessandro Perina, Nebojsa Jojic, Manuele Bicego, Andrzej Truski
• Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively Wen-Hao Zhang, Si Wu
• Latent Maximum Margin Clustering Guang-Tong Zhou, Tian Lan, Arash Vahdat, Greg Mori
• Data-driven Distributionally Robust Polynomial Optimization Martin Mevissen, Emanuele Ragnoli, Jia Yuan Yu
• Transfer Learning in a Transductive Setting Marcus Rohrbach, Sandra Ebert, Bernt Schiele
• Bayesian optimization explains human active search Ali Borji, Laurent Itti
• Provable Subspace Clustering: When LRR meets SSC Yu-Xiang Wang, Huan Xu, Chenlei Leng
• Generalized Random Utility Models with Multiple Types Hossein Azari Soufiani, Hansheng Diao, Zhenyu Lai, David C. Parkes
• Polar Operators for Structured Sparse Estimation Xinhua Zhang, Yao-Liang Yu, Dale Schuurmans
• On Decomposing the Proximal Map Yao-Liang Yu
• Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs Liam C. MacDermed, Charles Isbell
• PAC-Bayes-Empirical-Bernstein Inequality Ilya O. Tolstikhin, Yevgeny Seldin
• Modeling Clutter Perception using Parametric Proto-object Partitioning Chen-Ping Yu, Wen-Yu Hua, Dimitris Samaras, Greg Zelinsky
• Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching Marcelo Fiori, Pablo Sprechmann, Joshua Vogelstein, Pablo Muse, Guillermo Sapiro
• Transportability from Multiple Environments with Limited Experiments Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl
• More data speeds up training time in learning halfspaces over sparse vectors Amit Daniely, Nati Linial, Shai Shalev-Shwartz
• Causal Inference on Time Series using Restricted Structural Equation Models Jonas Peters, Dominik Janzing, Prof. Bernhard Schölkopf
• Deep Fisher Networks for Large-Scale Image Classification Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
• Sparse Additive Text Models with Low Rank Background Lei Shi
• Variance Reduction for Stochastic Gradient Optimization Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing
• Training and Analysing Deep Recurrent Neural Networks Michiel Hermans, Benjamin Schrauwen
• A simple example of Dirichlet process mixture inconsistency for the number of components Jeffrey W. Miller, Matthew T. Harrison
• Variational Policy Search via Trajectory Optimization Sergey Levine, Vladlen Koltun
• Scalable kernels for graphs with continuous attributes Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, Karsten Borgwardt
• Density estimation from unweighted k-nearest neighbor graphs: a roadmap Ulrike Von Luxburg, Morteza Alamgir
• Decision Jungles: Compact and Rich Models for Classification Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi
• What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach Zhenwen Dai, Georgios Exarchakis, Jörg Lücke
• Actor-Critic Algorithms for Risk-Sensitive MDPs Prashanth L.A., Mohammad Ghavamzadeh
• Summary Statistics for Partitionings and Feature Allocations Isik B. Fidaner, Taylan Cemgil
• One-shot learning and big data with n=2 Lee H. Dicker, Dean P. Foster
• Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression Michalis Titsias RC AUEB, Miguel Lazaro-Gredilla
• Correlations strike back (again): the case of associative memory retrieval Cristina Savin, Peter Dayan, Mate Lengyel
• Optimal Neural Population Codes for High-dimensional Stimulus Variables Zhuo Wang, Alan A. Stocker, Daniel D. Lee
• Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking Ryan D. Turner, Steven Bottone, Clay J. Stanek
• Accelerating Stochastic Gradient Descent using Predictive Variance Reduction Rie Johnson, Tong Zhang
• Using multiple samples to learn mixture models Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
• Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition Tzu-Kuo Huang, Jeff Schneider
• On model selection consistency of penalized M-estimators: a geometric theory Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
• Dropout Training as Adaptive Regularization Stefan Wager, Sida Wang, Percy S. Liang
• New Subsampling Algorithms for Fast Least Squares Regression Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar
• Faster Ridge Regression via the Subsampled Randomized Hadamard Transform Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar
• Accelerated Mini-Batch Stochastic Dual Coordinate Ascent Shai Shalev-Shwartz, Tong Zhang
• Improved and Generalized Upper Bounds on the Complexity of Policy Iteration Bruno Scherrer
• Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation Dahua Lin
• Online Robust PCA via Stochastic Optimization Jiashi Feng, Huan Xu, Shuicheng Yan
• Least Informative Dimensions Fabian Sinz, Anna Stockl, Jan Grewe, Jan Benda
• A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks Junming Yin, Qirong Ho, Eric P. Xing
• Understanding variable importances in forests of randomized trees Gilles Louppe, Louis Wehenkel, Antonio Sutera, Pierre Geurts
• Correlated random features for fast semi-supervised learning Brian McWilliams, David Balduzzi, Joachim M. Buhmann
• Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin
• Better Approximation and Faster Algorithm Using the Proximal Average Yao-Liang Yu
• Rapid Distance-Based Outlier Detection via Sampling Mahito Sugiyama, Karsten Borgwardt
• Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima Po-Ling Loh, Martin J. Wainwright
• Non-Linear Domain Adaptation with Boosting Carlos J. Becker, Christos M. Christoudias, Pascal Fua
• Mid-level Visual Element Discovery as Discriminative Mode Seeking Carl Doersch, Abhinav Gupta, Alexei A. Efros
• q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions Assaf Glazer, Michael Lindenbaum, Shaul Markovitch
• Auditing: Active Learning with Outcome-Dependent Query Costs Sivan Sabato, Anand D. Sarwate, Nati Srebro
• A message-passing algorithm for multi-agent trajectory planning Jose Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan S. Yedidia
• Learning Stochastic Feedforward Neural Networks Yichuan Tang, Ruslan R. Salakhutdinov
• Inferring neural population dynamics from multiple partial recordings of the same neural circuit Srini Turaga, Lars Buesing, Adam M. Packer, Henry Dalgleish, Noah Pettit, Michael Hausser, Jakob Macke
• Multi-Prediction Deep Boltzmann Machines Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
• Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation Vibhav Vineet, Carsten Rother, Philip Torr
• Blind Calibration in Compressed Sensing using Message Passing Algorithms Christophe Schulke, Francesco Caltagirone, Florent Krzakala, Lenka Zdeborová
• Learning Trajectory Preferences for Manipulators via Iterative Improvement Ashesh Jain, Brian Wojcik, Thorsten Joachims, Ashutosh Saxena
• Large Scale Distributed Sparse Precision Estimation Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Neural representation of action sequences: how far can a simple snippet-matching model take us? Cheston Tan, Jedediah M. Singer, Thomas Serre, David Sheinberg, Tomaso Poggio
• On Algorithms for Sparse Multi-factor NMF Siwei Lyu, Xin Wang
• Dirty Statistical Models Eunho Yang, Pradeep K. Ravikumar
• Parallel Sampling of DP Mixture Models using Sub-Cluster Splits Jason Chang, John W. Fisher III
• Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent Tianbao Yang
• Prior-free and prior-dependent regret bounds for Thompson Sampling Sebastien Bubeck, Che-Yu Liu
• Structured Learning via Logistic Regression Justin Domke
• Which Space Partitioning Tree to Use for Search? Parikshit Ram, Alexander Gray
• Projecting Ising Model Parameters for Fast Mixing Justin Domke, Xianghang Liu
• Mixed Optimization for Smooth Functions Mehrdad Mahdavi, Lijun Zhang, Rong Jin
• Conditional Random Fields via Univariate Exponential Families Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu
• Stochastic blockmodel approximation of a graphon: Theory and consistent estimation Edo M. Airoldi, Thiago B. Costa, Stanley H. Chan
• Reinforcement Learning in Robust Markov Decision Processes Shiau Hong Lim, Huan Xu, Shie Mannor
• On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization Ke Hou, Zirui Zhou, Anthony Man-Cho So, Zhi-Quan Luo
• Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems Hesham Mostafa, Lorenz. K. Mueller, Giacomo Indiveri
• Latent Structured Active Learning Wenjie Luo, Alex Schwing, Raquel Urtasun
• A Gang of Bandits Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella
• Learning Feature Selection Dependencies in Multi-task Learning Daniel Hernández-Lobato, José Miguel Hernández-Lobato
• B-test: A Non-parametric, Low Variance Kernel Two-sample Test Wojciech Zaremba, Arthur Gretton, Matthew Blaschko
• Online PCA for Contaminated Data Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
• Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) Francis Bach, Eric Moulines
• Efficient Algorithm for Privately Releasing Smooth Queries Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang
• Beyond Pairwise: Provably Fast Algorithms for Approximate k-Way Similarity Search Anshumali Shrivastava, Ping Li
• Unsupervised Spectral Learning of Finite State Transducers Raphael Bailly, Xavier Carreras, Ariadna Quattoni
• Learning a Deep Compact Image Representation for Visual Tracking Naiyan Wang, Dit-Yan Yeung
• Learning Multi-level Sparse Representations Ferran Diego Andilla, Fred A. Hamprecht
• Robust Data-Driven Dynamic Programming Grani Adiwena Hanasusanto, Daniel Kuhn
• Low-Rank Matrix and Tensor Completion via Adaptive Sampling Akshay Krishnamurthy, Aarti Singh
• Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms Adrien Todeschini, François Caron, Marie Chavent
• Distributed Exploration in Multi-Armed Bandits Eshcar Hillel, Zohar S. Karnin, Tomer Koren, Ronny Lempel, Oren Somekh
• The Pareto Regret Frontier Wouter M. Koolen
• Direct 0-1 Loss Minimization and Margin Maximization with Boosting Shaodan Zhai, Tian Xia, Ming Tan, Shaojun Wang
• Regret based Robust Solutions for Uncertain Markov Decision Processes Asrar Ahmed, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
• Speeding up Permutation Testing in Neuroimaging Chris Hinrichs, Vamsi K. Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh
• Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent
• Supervised Sparse Analysis and Synthesis Operators Pablo Sprechmann, Roee Litman, Tal Ben Yakar, Alexander M. Bronstein, Guillermo Sapiro
• Low-rank matrix reconstruction and clustering via approximate message passing Ryosuke Matsushita, Toshiyuki Tanaka
• Reasoning With Neural Tensor Networks for Knowledge Base Completion Richard Socher, Danqi Chen, Christopher D. Manning, Andrew Ng
• Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Christopher D. Manning, Andrew Ng
• Estimating LASSO Risk and Noise Level Mohsen Bayati, Murat A. Erdogdu, Andrea Montanari
• Learning Adaptive Value of Information for Structured Prediction David J. Weiss, Ben Taskar
• Efficient Online Inference for Bayesian Nonparametric Relational Models Dae Il Kim, Prem K. Gopalan, David Blei, Erik Sudderth
• Approximate inference in latent Gaussian-Markov models from continuous time observations Botond Cseke, Manfred Opper, Guido Sanguinetti
• Linear Convergence with Condition Number Independent Access of Full Gradients Lijun Zhang, Mehrdad Mahdavi, Rong Jin
• When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements Divyanshu Vats, Richard Baraniuk
• Wavelets on Graphs via Deep Learning Raif Rustamov, Leonidas J. Guibas
• Robust Spatial Filtering with Beta Divergence Wojciech Samek, Duncan Blythe, Klaus-Robert Müller, Motoaki Kawanabe
• Convex Relaxations for Permutation Problems Fajwel Fogel, Rodolphe Jenatton, Francis Bach, Alexandre D'Aspremont
• High-Dimensional Gaussian Process Bandits Josip Djolonga, Andreas Krause, Volkan Cevher
• A memory frontier for complex synapses Subhaneil Lahiri, Surya Ganguli
• Marginals-to-Models Reducibility Tim Roughgarden, Michael Kearns
• First-order Decomposition Trees Nima Taghipour, Jesse Davis, Hendrik Blockeel
• A Comparative Framework for Preconditioned Lasso Algorithms Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jordan
• Lasso Screening Rules via Dual Polytope Projection Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye
• Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent Yuening Hu, Jordan L. Boyd-Graber, Hal Daume III, Z. Irene Ying
• A Latent Source Model for Nonparametric Time Series Classification George H. Chen, Stanislav Nikolov, Devavrat Shah
• Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao, Marcus A. Brubaker, David J. Fleet, Aaron Hertzmann
• Lexical and Hierarchical Topic Regression Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik
• Stochastic Convex Optimization with Multiple Objectives Mehrdad Mahdavi, Tianbao Yang, Rong Jin
• A Kernel Test for Three-Variable Interactions Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
• Memoized Online Variational Inference for Dirichlet Process Mixture Models Michael C. Hughes, Erik Sudderth
• Designed Measurements for Vector Count Data Liming Wang, David E. Carlson, Miguel Rodrigues, David Wilcox, Robert Calderbank, Lawrence Carin
• Robust Transfer Principal Component Analysis with Rank Constraints Yuhong Guo
• Online Learning with Switching Costs and Other Adaptive Adversaries Nicolò Cesa-Bianchi, Ofer Dekel, Ohad Shamir
• Learning Prices for Repeated Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
• Probabilistic Principal Geodesic Analysis Miaomiao Zhang, P.T. Fletcher
• Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models Adel Javanmard, Andrea Montanari
• Learning with Noisy Labels Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep K. Ravikumar, Ambuj Tewari
• Tracking Time-varying Graphical Structure Erich Kummerfeld, David Danks
• Factorized Asymptotic Bayesian Inference for Latent Feature Models Kohei Hayashi, Ryohei Fujimaki
• More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B. Gibbons, Garth A. Gibson, Greg Ganger, Eric P. Xing
• Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models Jie Liu, David Page
• Online Learning with Costly Features and Labels Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari
• Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions Eftychios A. Pnevmatikakis, Liam Paninski
• A Novel Two-Step Method for Cross Language Representation Learning Min Xiao, Yuhong Guo
• On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations Tamir Hazan, Subhransu Maji, Tommi Jaakkola
• Graphical Models for Inference with Missing Data Karthika Mohan, Judea Pearl, Jin Tian
• Reshaping Visual Datasets for Domain Adaptation Boqing Gong, Kristen Grauman, Fei Sha
• Statistical Active Learning Algorithms Maria-Florina F. Balcan, Vitaly Feldman
• Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski
• Reflection methods for user-friendly submodular optimization Stefanie Jegelka, Francis Bach, Suvrit Sra
• Unsupervised Structure Learning of Stochastic And-Or Grammars Kewei Tu, Maria Pavlovskaia, Song-Chun Zhu
• Convex Tensor Decomposition via Structured Schatten Norm Regularization Ryota Tomioka, Taiji Suzuki
• Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs Yann Dauphin, Yoshua Bengio
• Learning Chordal Markov Networks by Constraint Satisfaction Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik Nyman, Johan Pensar
• Parametric Task Learning Ichiro Takeuchi, Tatsuya Hongo, Masashi Sugiyama, Shinichi Nakajima
• A Deep Architecture for Matching Short Texts Zhengdong Lu, Hang Li
• Computing the Stationary Distribution Locally Christina E. Lee, Asuman Ozdaglar, Devavrat Shah
• Nonparametric Multi-group Membership Model for Dynamic Networks Myunghwan Kim, Jure Leskovec
• Adaptive Step-Size for Policy Gradient Methods Matteo Pirotta, Marcello Restelli, Luca Bascetta
• Optimistic Concurrency Control for Distributed Unsupervised Learning Xinghao Pan, Joseph E. Gonzalez, Stefanie Jegelka, Tamara Broderick, Michael I. Jordan
• Reservoir Boosting : Between Online and Offline Ensemble Learning Leonidas Lefakis, François Fleuret
• Multiclass Total Variation Clustering Xavier Bresson, Thomas Laurent, David Uminsky, James von Brecht
• Approximate Inference in Continuous Determinantal Processes Raja Hafiz Affandi, Emily Fox, Ben Taskar
• Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering Shinichi Nakajima, Akiko Takeda, S. Derin Babacan, Masashi Sugiyama, Ichiro Takeuchi
• Thompson Sampling for 1-Dimensional Exponential Family Bandits Nathaniel Korda, Emilie Kaufmann, Remi Munos
• Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, Hai Leong Chieu
• It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals Barbara Rakitsch, Christoph Lippert, Karsten Borgwardt, Oliver Stegle
• Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari
• Inverse Density as an Inverse Problem: the Fredholm Equation Approach Qichao Que, Mikhail Belkin
• Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising Forest Agostinelli, Michael R. Anderson, Honglak Lee
• EDML for Learning Parameters in Directed and Undirected Graphical Models Khaled S. Refaat, Arthur Choi, Adnan Darwiche
• Similarity Component Analysis Soravit Changpinyo, Kuan Liu, Fei Sha
• Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Josh Tenenbaum
• Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation John Duchi, Martin J. Wainwright, Michael I. Jordan
• Firing rate predictions in optimal balanced networks David G. Barrett, Sophie Denève, Christian K. Machens
• Manifold-based Similarity Adaptation for Label Propagation Masayuki Karasuyama, Hiroshi Mamitsuka
• Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty Haichao Zhang, David Wipf
• Near-Optimal Entrywise Sampling for Data Matrices Dimitris Achlioptas, Zohar S. Karnin, Edo Liberty
• Learning to Prune in Metric and Non-Metric Spaces Leonid Boytsov, Bilegsaikhan Naidan
• Online learning in episodic Markovian decision processes by relative entropy policy search Alexander Zimin, Gergely Neu
• Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result Paul Wagner
• Bayesian Hierarchical Community Discovery Charles Blundell, Yee Whye Teh
• From Bandits to Experts: A Tale of Domination and Independence Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour
• Predictive PAC Learning and Process Decompositions Cosma Shalizi, Aryeh Kontorovich
• Pass-efficient unsupervised feature selection Crystal Maung, Haim Schweitzer
• Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors Xiaoqin Zhang, Di Wang, Zhengyuan Zhou, Yi Ma
• Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search Aijun Bai, Feng Wu, Xiaoping Chen
• Solving inverse problem of Markov chain with partial observations Tetsuro Morimura, Takayuki Osogami, Tsuyoshi Ide
• Locally Adaptive Bayesian Multivariate Time Series Daniele Durante, Bruno Scarpa, David B. Dunson
• Mapping paradigm ontologies to and from the brain Yannick Schwartz, Bertrand Thirion, Gael Varoquaux
• Noise-Enhanced Associative Memories Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi, Lav R. Varshney
• Exact and Stable Recovery of Pairwise Interaction Tensors Shouyuan Chen, Michael R. Lyu, Irwin King, Zenglin Xu
• Bayesian entropy estimation for binary spike train data using parametric prior knowledge Evan W. Archer, Il Memming Park, Jonathan W. Pillow
• Perfect Associative Learning with Spike-Timing-Dependent Plasticity Christian Albers, Maren Westkott, Klaus Pawelzik
• On Poisson Graphical Models Eunho Yang, Pradeep K. Ravikumar, Genevera I. Allen, Zhandong Liu
• Streaming Variational Bayes Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan
• Gaussian Process Conditional Copulas with Applications to Financial Time Series José Miguel Hernández-Lobato, James R. Lloyd, Daniel Hernández-Lobato
• Extracting regions of interest from biological images with convolutional sparse block coding Marius Pachitariu, Adam M. Packer, Noah Pettit, Henry Dalgleish, Michael Hausser, Maneesh Sahani
• Approximate Dynamic Programming Finally Performs Well in the Game of Tetris Victor Gabillon, Mohammad Ghavamzadeh, Bruno Scherrer
• Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections Matthew Lawlor, Steven W. Zucker
• DESPOT: Online POMDP Planning with Regularization Adhiraj Somani, Nan Ye, David Hsu, Wee Sun Lee
• Matrix Completion From any Given Set of Observations Troy Lee, Adi Shraibman
• Regression-tree Tuning in a Streaming Setting Samory Kpotufe, Francesco Orabona
• Multiscale Dictionary Learning for Estimating Conditional Distributions Francesca Petralia, Joshua T. Vogelstein, David B. Dunson
• Dimension-Free Exponentiated Gradient Francesco Orabona
• Stochastic Optimization of PCA with Capped MSG Raman Arora, Andy Cotter, Nati Srebro
• On Flat versus Hierarchical Classification in Large-Scale Taxonomies Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-Reza Amini
• Learning Gaussian Graphical Models with Observed or Latent FVSs Ying Liu, Alan Willsky
• Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies Yangqing Jia, Joshua T. Abbott, Joseph L. Austerweil, Thomas Griffiths, Trevor Darrell
• Robust Bloom Filters for Large MultiLabel Classification Tasks Moustapha M. Cisse, Nicolas Usunier, Thierry Artières, Patrick Gallinari
• Solving the multi-way matching problem by permutation synchronization Deepti Pachauri, Risi Kondor, Vikas Singh
• Generalizing Analytic Shrinkage for Arbitrary Covariance Structures Daniel Bartz, Klaus-Robert Müller
• Top-Down Regularization of Deep Belief Networks Hanlin Goh, Nicolas Thome, Matthieu Cord, Joo-Hwee Lim
• Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions Tamir Hazan, Subhransu Maji, Joseph Keshet, Tommi Jaakkola
• Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms Yu Zhang
• Machine Teaching for Bayesian Learners in the Exponential Family Xiaojin Zhu
• Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? Qiang Liu, Alexander T. Ihler, Mark Steyvers
• Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths Stefan Mathe, Cristian Sminchisescu
• A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek, Richard Zemel, Ryan P. Adams
• Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model Fang Han, Han Liu
• Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation Bogdan Savchynskyy, Jörg Hendrik Kappes, Paul Swoboda, Christoph Schnörr
• Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic James L. Sharpnack, Akshay Krishnamurthy, Aarti Singh
• Demixing odors - fast inference in olfaction Agnieszka Grabska-Barwinska, Jeff Beck, Alexandre Pouget, Peter Latham
• Learning Multiple Models via Regularized Weighting Daniel Vainsencher, Shie Mannor, Huan Xu
• When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity Anima Anandkumar, Daniel J. Hsu, Majid Janzamin, Sham M. Kakade
• Distributed k-means and k-median Clustering on General Topologies Maria-Florina F. Balcan, Steven Ehrlich, Yingyu Liang
• Multi-Task Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan P. Adams
• Online Learning of Dynamic Parameters in Social Networks Shahin Shahrampour, Sasha Rakhlin, Ali Jadbabaie
• A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles Jinwoo Shin, Andrew E. Gelfand, Misha Chertkov
• Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space Xinhua Zhang, Wee Sun Lee, Yee Whye Teh
• Approximate Gaussian process inference for the drift function in stochastic differential equations Andreas Ruttor, Philipp Batz, Manfred Opper
• Distributed Submodular Maximization: Identifying Representative Elements in Massive Data Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause
• Adaptive Market Making via Online Learning Jacob Abernethy, Satyen Kale
• On the Sample Complexity of Subspace Learning Alessandro Rudi, Guillermo D. Canas, Lorenzo Rosasco
• Spike train entropy-rate estimation using hierarchical Dirichlet process priors Karin C. Knudson, Jonathan W. Pillow
• Embed and Project: Discrete Sampling with Universal Hashing Stefano Ermon, Carla P. Gomes, Ashish Sabharwal, Bart Selman
• Discriminative Transfer Learning with Tree-based Priors Nitish Srivastava, Ruslan R. Salakhutdinov
• Small-Variance Asymptotics for Hidden Markov Models Anirban Roychowdhury, Ke Jiang, Brian Kulis
• Convergence of Monte Carlo Tree Search in Simultaneous Move Games Viliam Lisy, Vojta Kovarik, Marc Lanctot, Branislav Bosansky
• DeViSE: A Deep Visual-Semantic Embedding Model Andrea Frome, Greg S. Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, Tomas Mikolov
• Reward Mapping for Transfer in Long-Lived Agents Xiaoxiao Guo, Satinder Singh, Richard L. Lewis
• Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation Martin Azizyan, Aarti Singh, Larry Wasserman
• Predicting Parameters in Deep Learning Misha Denil, Babak Shakibi, Laurent Dinh, Marc'Aurelio Ranzato, Nando de Freitas
• Estimating the Unseen: Improved Estimators for Entropy and other Properties Paul Valiant, Gregory Valiant
• What do row and column marginals reveal about your dataset? Behzad Golshan, John Byers, Evimaria Terzi
• RNADE: The real-valued neural autoregressive density-estimator Benigno Uria, Iain Murray, Hugo Larochelle
• Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards Thomas Bonald, Alexandre Proutiere
• Reconciling "priors" & "priors" without prejudice? Remi Gribonval, Pierre Machart
• Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis Nikhil Rao, Christopher Cox, Rob Nowak, Timothy T. Rogers
• Sensor Selection in High-Dimensional Gaussian Trees with Nuisances Daniel S. Levine, Jonathan P. How
• Sequential Transfer in Multi-armed Bandit with Finite Set of Models Mohammad Gheshlaghi azar, Alessandro Lazaric, Emma Brunskill
• Buy-in-Bulk Active Learning Liu Yang, Jaime Carbonell
• Contrastive Learning Using Spectral Methods James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams
• Message Passing Inference with Chemical Reaction Networks Nils E. Napp, Ryan P. Adams
• Eluder Dimension and the Sample Complexity of Optimistic Exploration Dan Russo, Benjamin Van Roy
• Learning word embeddings efficiently with noise-contrastive estimation Andriy Mnih, Koray Kavukcuoglu
• Sparse Inverse Covariance Estimation with Calibration Tuo Zhao, Han Liu
• Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization Julien Mairal
• Sinkhorn Distances: Lightspeed Computation of Optimal Transport Marco Cuturi
• Speedup Matrix Completion with Side Information: Application to Multi-Label Learning Miao Xu, Rong Jin, Zhi-Hua Zhou
• Compete to Compute Rupesh K. Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino Gomez, Juergen Schmidhuber
• Fast Determinantal Point Process Sampling with Application to Clustering Byungkon Kang
• Information-theoretic lower bounds for distributed statistical estimation with communication constraints Yuchen Zhang, John Duchi, Michael I. Jordan, Martin J. Wainwright
• Projected Natural Actor-Critic Philip S. Thomas, William C. Dabney, Stephen Giguere, Sridhar Mahadevan
• How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal Jacob Abernethy, Peter L. Bartlett, Rafael Frongillo, Andre Wibisono
• Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests Yacine Jernite, Yonatan Halpern, David Sontag
• Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion Franz Kiraly, Louis Theran
• Learning the Local Statistics of Optical Flow Dan Rosenbaum, Daniel Zoran, Yair Weiss
• Aggregating Optimistic Planning Trees for Solving Markov Decision Processes Gunnar Kedenburg, Raphael Fonteneau, Remi Munos
• Robust learning of low-dimensional dynamics from large neural ensembles David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
• Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising Min Xu, Tao Qin, Tie-Yan Liu
• Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization Nataliya Shapovalova, Michalis Raptis, Leonid Sigal, Greg Mori
• A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables Jing Xiang, Seyoung Kim
• The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram
• Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints Rishabh K. Iyer, Jeff A. Bilmes
• Scalable Inference for Logistic-Normal Topic Models Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng, Bo Zhang
• Spectral methods for neural characterization using generalized quadratic models Il Memming Park, Evan W. Archer, Nicholas Priebe, Jonathan W. Pillow
• Universal models for binary spike patterns using centered Dirichlet processes Il Memming Park, Evan W. Archer, Kenneth Latimer, Jonathan W. Pillow
• Synthesizing Robust Plans under Incomplete Domain Models Tuan A. Nguyen, Subbarao Kambhampati, Minh Do
• Integrated Non-Factorized Variational Inference Shaobo Han, Xuejun Liao, Lawrence Carin
• Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions Ari Pakman, Liam Paninski
• Symbolic Opportunistic Policy Iteration for Factored-Action MDPs Aswin Raghavan, Roni Khardon, Alan Fern, Prasad Tadepalli
• Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions Yasin Abbasi, Peter L. Bartlett, Varun Kanade, Yevgeny Seldin, Csaba Szepesvari
• Flexible sampling of discrete data correlations without the marginal distributions Alfredo Kalaitzis, Ricardo Silva
• One-shot learning by inverting a compositional causal process Brenden M. Lake, Ruslan R. Salakhutdinov, Josh Tenenbaum
• Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. Michel Besserve, Nikos K. Logothetis, Prof. Bernhard Schölkopf
• Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis James R. Voss, Luis Rademacher, Mikhail Belkin
• Deep Neural Networks for Object Detection Christian Szegedy, Alexander Toshev, Dumitru Erhan
• Geometric optimisation on positive definite matrices for elliptically contoured distributions Suvrit Sra, Reshad Hosseini
• Sign Cauchy Projections and Chi-Square Kernel Ping Li, Gennady Samorodnitsk, John Hopcroft
• Relevance Topic Model for Unstructured Social Group Activity Recognition Fang Zhao, Yongzhen Huang, Liang Wang, Tieniu Tan
• k-Prototype Learning for 3D Rigid Structures Hu Ding, Ronald Berezney, Jinhui Xu
• Restricting exchangeable nonparametric distributions Sinead A. Williamson, Steve N. MacEachern, Eric P. Xing
• Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting Shunan Zhang, Angela J. Yu
• Probabilistic Movement Primitives Alexandros Paraschos, Christian Daniel, Jan R. Peters, Gerhard Neumann
• Policy Shaping: Integrating Human Feedback with Reinforcement Learning Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles Isbell, Andrea L. Thomaz
• Multilinear Dynamical Systems for Tensor Time Series Mark Rogers, Lei Li, Stuart J. Russell
• Deep content-based music recommendation Aaron van den Oord, Sander Dieleman, Benjamin Schrauwen
• A Stability-based Validation Procedure for Differentially Private Machine Learning Kamalika Chaudhuri, Staal A. Vinterbo
• Capacity of strong attractor patterns to model behavioural and cognitive prototypes Abbas Edalat
• Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA Vincent Q. Vu, Juhee Cho, Jing Lei, Karl Rohe
• Cluster Trees on Manifolds Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry Wasserman
• Bayesian inference for low rank spatiotemporal neural receptive fields Mijung Park, Jonathan W. Pillow
• Adaptive Submodular Maximization in Bandit Setting Victor Gabillon, Branislav Kveton, Zheng Wen, Brian Eriksson, S. Muthukrishnan
• Generalized Method-of-Moments for Rank Aggregation Hossein Azari Soufiani, William Chen, David C. Parkes, Lirong Xia
• Analyzing Hogwild Parallel Gaussian Gibbs Sampling Matthew Johnson, James Saunderson, Alan Willsky
• Minimax Optimal Algorithms for Unconstrained Linear Optimization Brendan McMahan, Jacob Abernethy
• (Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings Abhradeep Guha Thakurta, Adam Smith
• Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes
• Σ-Optimality for Active Learning on Gaussian Random Fields Yifei Ma, Roman Garnett, Jeff Schneider
• Learning Kernels Using Local Rademacher Complexity Corinna Cortes, Marius Kloft, Mehryar Mohri
• Annealing between distributions by averaging moments Roger B. Grosse, Chris J. Maddison, Ruslan R. Salakhutdinov
• Optimizing Instructional Policies Robert V. Lindsey, Michael C. Mozer, William J. Huggins, Harold Pashler
• Translating Embeddings for Modeling Multi-relational Data Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko
• Phase Retrieval using Alternating Minimization Praneeth Netrapalli, Prateek Jain, Sujay Sanghavi
• Real-Time Inference for a Gamma Process Model of Neural Spiking David E. Carlson, Vinayak Rao, Joshua T. Vogelstein, Lawrence Carin
• Understanding Dropout Pierre Baldi, Peter J. Sadowski
• The Power of Asymmetry in Binary Hashing Behnam Neyshabur, Nati Srebro, Ruslan R. Salakhutdinov, Yury Makarychev, Payman Yadollahpour
• Estimation, Optimization, and Parallelism when Data is Sparse John Duchi, Michael I. Jordan, Brendan McMahan
• A multi-agent control framework for co-adaptation in brain-computer interfaces Josh S. Merel, Roy Fox, Tony Jebara, Liam Paninski
• Modeling Overlapping Communities with Node Popularities Prem K. Gopalan, Chong Wang, David Blei
• Learning from Limited Demonstrations Beomjoon Kim, Amir massoud Farahmand, Joelle Pineau, Doina Precup
• On the Complexity and Approximation of Binary Evidence in Lifted Inference Guy Van den Broeck, Adnan Darwiche
• On the Representational Efficiency of Restricted Boltzmann Machines James Martens, Arkadev Chattopadhya, Toni Pitassi, Richard Zemel
• Memory Limited, Streaming PCA Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain
• An Approximate, Efficient LP Solver for LP Rounding Srikrishna Sridhar, Stephen Wright, Christopher Re, Ji Liu, Victor Bittorf, Ce Zhang
• Linear decision rule as aspiration for simple decision heuristics Özgür Şimşek
• On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation Harikrishna Narasimhan, Shivani Agarwal
• Bayesian inference as iterated random functions with applications to sequential inference in graphical models Arash Amini, XuanLong Nguyen
• Compressive Feature Learning Hristo S. Paskov, Robert West, John C. Mitchell, Trevor Hastie
• Moment-based Uniform Deviation Bounds for k-means and Friends Matus J. Telgarsky, Sanjoy Dasgupta
• Fast Template Evaluation with Vector Quantization Mohammad Amin Sadeghi, David Forsyth
• Context-sensitive active sensing in humans Sheeraz Ahmad, He Huang, Angela J. Yu
• A New Convex Relaxation for Tensor Completion Bernardino Romera-Paredes, Massimiliano Pontil
• Variational Planning for Graph-based MDPs Qiang Cheng, Qiang Liu, Feng Chen, Alexander T. Ihler
• Convex Two-Layer Modeling Özlem Aslan, Hao Cheng, Xinhua Zhang, Dale Schuurmans
• Sketching Structured Matrices for Faster Nonlinear Regression Haim Avron, Vikas Sindhwani, David Woodruff
• (More) Efficient Reinforcement Learning via Posterior Sampling Ian Osband, Dan Russo, Benjamin Van Roy
• Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition Adel Javanmard, Andrea Montanari
• Efficient Exploration and Value Function Generalization in Deterministic Systems Zheng Wen, Benjamin Van Roy
• Bellman Error Based Feature Generation using Random Projections on Sparse Spaces Mahdi Milani Fard, Yuri Grinberg, Amir massoud Farahmand, Joelle Pineau, Doina Precup
• Learning and using language via recursive pragmatic reasoning about other agents Nathaniel J. Smith, Noah Goodman, Michael Frank
• Learning Stochastic Inverses Andreas Stuhlmüller, Jacob Taylor, Noah Goodman
• Learning invariant representations and applications to face verification Qianli Liao, Joel Z. Leibo, Tomaso Poggio
• Optimization, Learning, and Games with Predictable Sequences Sasha Rakhlin, Karthik Sridharan
• Adaptivity to Local Smoothness and Dimension in Kernel Regression Samory Kpotufe, Vikas Garg
• Adaptive dropout for training deep neural networks Jimmy Ba, Brendan Frey
• Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral StreamDaniel L. Yamins, Ha Hong, Charles Cadieu, James J. DiCarlo
• Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex Sam Patterson, Yee Whye Teh
• Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, Jeff Dean
• Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel Tai Qin, Karl Rohe
• Analyzing the Harmonic Structure in Graph-Based Learning Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang
• Recurrent linear models of simultaneously-recorded neural populations Marius Pachitariu, Biljana Petreska, Maneesh Sahani
• Scalable Influence Estimation in Continuous-Time Diffusion Networks Nan Du, Le Song, Manuel Gomez Rodriguez, Hongyuan Zha
• Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl Edward Rasmussen
• BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep K. Ravikumar, Russell Poldrack
• The Fast Convergence of Incremental PCA Akshay Balsubramani, Sanjoy Dasgupta, Yoav Freund
• Multisensory Encoding, Decoding, and Identification Aurel A. Lazar, Yevgeniy Slutskiy
• Adaptive Anonymity via b-Matching Krzysztof M. Choromanski, Tony Jebara, Kui Tang
• Optimal integration of visual speed across different spatiotemporal frequency channels Matjaz Jogan, Alan A. Stocker
• Matrix factorization with binary components Martin Slawski, Matthias Hein, Pavlo Lutsik
• Learning to Pass Expectation Propagation Messages Nicolas Heess, Daniel Tarlow, John Winn
• Robust Low Rank Kernel Embeddings of Multivariate Distributions Le Song, Bo Dai

Advances in Neural Information Processing Systems 27 (NIPS 2014)

The papers below appear in Advances in Neural Information Processing Systems 27 edited by  Z. Ghahramani  and  M. Welling  and  C. Cortes  and  N.D. Lawrence  and  K.Q. Weinberger .
They are proceedings from the conference, "Neural Information Processing Systems 2014."

• Kernel Mean Estimation via Spectral Filtering Krikamol Muandet, Bharath Sriperumbudur, Prof. Bernhard Schölkopf
• Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang, Charles Sutton
• Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li, David G. Andersen, Alexander J. Smola, Kai Yu
• The Infinite Mixture of Infinite Gaussian Mixtures Halid Z. Yerebakan, Bartek Rajwa, Murat Dundar
• Robust Classification Under Sample Selection Bias Anqi Liu, Brian Ziebart
• Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang
• Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction Katerina Fragkiadaki, Marta Salas, Pablo Arbelaez, Jitendra Malik
• Sparse Space-Time Deconvolution for Calcium Image Analysis Ferran Diego Andilla, Fred A. Hamprecht
• Restricted Boltzmann machines modeling human choice Takayuki Osogami, Makoto Otsuka
• Multiscale Fields of Patterns Pedro Felzenszwalb, John G. Oberlin
• large scale canonical correlation analysis with iterative least squares Yichao Lu, Dean P. Foster
• Altitude Training: Strong Bounds for Single-Layer Dropout Stefan Wager, William Fithian, Sida Wang, Percy S. Liang
• Rounding-based Moves for Metric Labeling M. Pawan Kumar
• Parallel Double Greedy Submodular Maximization Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan
• Multivariate Regression with Calibration Han Liu, Lie Wang, Tuo Zhao
• Exact Post Model Selection Inference for Marginal Screening Jason D. Lee, Jonathan E. Taylor
• On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang
• Just-In-Time Learning for Fast and Flexible Inference S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn
• Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation Ohad Shamir
• Quantized Kernel Learning for Feature Matching Danfeng Qin, Xuanli Chen, Matthieu Guillaumin, Luc V. Gool
• Parallel Direction Method of Multipliers Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
• (Almost) No Label No Cry Giorgio Patrini, Richard Nock, Paul Rivera, Tiberio Caetano
• Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards Omar Besbes, Yonatan Gur, Assaf Zeevi
• Object Localization based on Structural SVM using Privileged Information Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han
• Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
• Shape and Illumination from Shading using the Generic Viewpoint Assumption Daniel Zoran, Dilip Krishnan, Jose Bento, Bill Freeman
• Parallel Sampling of HDPs using Sub-Cluster Splits Jason Chang, John W. Fisher III
• From MAP to Marginals: Variational Inference in Bayesian Submodular Models Josip Djolonga, Andreas Krause
• Robust Logistic Regression and Classification Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
• Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities Tianbao Yang, Rong Jin
• A Unified Semantic Embedding: Relating Taxonomies and Attributes Sung Ju Hwang, Leonid Sigal
• Transportability from Multiple Environments with Limited Experiments: Completeness Results Elias Bareinboim, Judea Pearl
• Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
• Causal Inference through a Witness Protection Program Ricardo Silva, Robin Evans
• Incremental Clustering: The Case for Extra Clusters Margareta Ackerman, Sanjoy Dasgupta
• Multi-scale Graphical Models for Spatio-Temporal Processes firdaus janoos, Huseyin Denli, Niranjan Subrahmanya
• Iterative Neural Autoregressive Distribution Estimator NADE-k Tapani Raiko, Yao Li, Kyunghyun Cho, Yoshua Bengio
• Sparse PCA via Covariance Thresholding Yash Deshpande, Andrea Montanari
• Low-dimensional models of neural population activity in sensory cortical circuits Evan W. Archer, Urs Koster, Jonathan W. Pillow, Jakob H. Macke
• A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System Yuanyuan Mi, Luozheng Li, Dahui Wang, Si Wu
• A Representation Theory for Ranking Functions Harsh H. Pareek, Pradeep K. Ravikumar
• Near-optimal sample compression for nearest neighbors Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch
• Combinatorial Pure Exploration of Multi-Armed Bandits Shouyuan Chen, Tian Lin, Irwin King, Michael R. Lyu, Wei Chen
• Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces Minh Ha Quang, Marco San Biagio, Vittorio Murino
• Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model Debarghya Ghoshdastidar, Ambedkar Dukkipati
• Spectral Clustering of graphs with the Bethe Hessian Alaa Saade, Florent Krzakala, Lenka Zdeborová
• Fast and Robust Least Squares Estimation in Corrupted Linear Models Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann
• Local Decorrelation For Improved Pedestrian Detection Woonhyun Nam, Piotr Dollar, Joon Hee Han
• Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space Robert A. Vandermeulen, Clayton Scott
• Beyond Disagreement-Based Agnostic Active Learning Chicheng Zhang, Kamalika Chaudhuri
• Bayes-Adaptive Simulation-based Search with Value Function Approximation Arthur Guez, Nicolas Heess, David Silver, Peter Dayan
• A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment Sahar Akram, Jonathan Z. Simon, Shihab A. Shamma, Behtash Babadi
• Active Regression by Stratification Sivan Sabato, Remi Munos
• Sensory Integration and Density Estimation Joseph G. Makin, Philip N. Sabes
• Learning Deep Features for Scene Recognition using Places Database Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva
• A Complete Variational Tracker Ryan D. Turner, Steven Bottone, Bhargav Avasarala
• Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks Yuanyuan Mi, C. C. Alan Fung, K. Y. Michael Wong, Si Wu
• Efficient Sampling for Learning Sparse Additive Models in High Dimensions Hemant Tyagi, Bernd Gärtner, Andreas Krause
• Deep Joint Task Learning for Generic Object Extraction Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, Wangmeng Zuo
• Robust Bayesian Max-Margin Clustering Changyou Chen, Jun Zhu, Xinhua Zhang
• Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision Deepti Pachauri, Risi Kondor, Gautam Sargur, Vikas Singh
• Bounded Regret for Finite-Armed Structured Bandits Tor Lattimore, Remi Munos
• Coresets for k-Segmentation of Streaming Data Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, Daniela Rus
• Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan, Andrew Zisserman
• Discovering Structure in High-Dimensional Data Through Correlation Explanation Greg Ver Steeg, Aram Galstyan
• Positive Curvature and Hamiltonian Monte Carlo Christof Seiler, Simon Rubinstein-Salzedo, Susan Holmes
• Learning Mixed Multinomial Logit Model from Ordinal Data Sewoong Oh, Devavrat Shah
• Near-optimal Reinforcement Learning in Factored MDPs Ian Osband, Benjamin Van Roy
• Efficient learning by implicit exploration in bandit problems with side observations Tomáš Kocák, Gergely Neu, Michal Valko, Remi Munos
• Repeated Contextual Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
• Recursive Inversion Models for Permutations Christopher Meek, Marina Meila
• On the Convergence Rate of Decomposable Submodular Function Minimization Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
• New Rules for Domain Independent Lifted MAP Inference Happy Mittal, Prasoon Goyal, Vibhav G. Gogate, Parag Singla
• PAC-Bayesian AUC classification and scoring James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang
• Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection Sang Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam
• On Prior Distributions and Approximate Inference for Structured Variables Oluwasanmi O. Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack
• On Iterative Hard Thresholding Methods for High-dimensional M-Estimation Prateek Jain, Ambuj Tewari, Purushottam Kar
• Online and Stochastic Gradient Methods for Non-decomposable Loss Functions Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
• Analysis of Learning from Positive and Unlabeled Data Marthinus C. du Plessis, Gang Niu, Masashi Sugiyama
• Dimensionality Reduction with Subspace Structure Preservation Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju
• Constrained convex minimization via model-based excessive gap Quoc Tran-Dinh, Volkan Cevher
• Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data Florian Stimberg, Andreas Ruttor, Manfred Opper
• Probabilistic ODE Solvers with Runge-Kutta Means Michael Schober, David K. Duvenaud, Philipp Hennig
• Optimal decision-making with time-varying evidence reliability Jan Drugowitsch, Ruben Moreno-Bote, Alexandre Pouget
• Learning Shuffle Ideals Under Restricted Distributions Dongqu Chen
• Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
• Distance-Based Network Recovery under Feature Correlation David Adametz, Volker Roth
• Bandit Convex Optimization: Towards Tight Bounds Elad Hazan, Kfir Levy
• Projective dictionary pair learning for pattern classification Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng
• Provable Submodular Minimization using Wolfe's Algorithm Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari
• Exploiting easy data in online optimization Amir Sani, Gergely Neu, Alessandro Lazaric
• Sparse Multi-Task Reinforcement Learning Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
• Best-Arm Identification in Linear Bandits Marta Soare, Alessandro Lazaric, Remi Munos
• Mind the Nuisance: Gaussian Process Classification using Privileged Noise Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto
• Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology Remi Lemonnier, Kevin Scaman, Nicolas Vayatis
• On the Computational Efficiency of Training Neural Networks Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
• Self-Adaptable Templates for Feature Coding Xavier Boix, Gemma Roig, Salomon Diether, Luc V. Gool
• Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-Taylor
• Stochastic Network Design in Bidirected Trees xiaojian wu, Daniel R. Sheldon, Shlomo Zilberstein
• Learning convolution filters for inverse covariance estimation of neural network connectivity George Mohler
• SerialRank: Spectral Ranking using Seriation Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
• Clamping Variables and Approximate Inference Adrian Weller, Tony Jebara
• Predictive Entropy Search for Efficient Global Optimization of Black-box Functions José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
• A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation Eran Treister, Javier S. Turek
• Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials Shenlong Wang, Alex Schwing, Raquel Urtasun
• Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich
• Inferring synaptic conductances from spike trains with a biophysically inspired point process model Kenneth W. Latimer, E. J. Chichilnisky, Fred Rieke, Jonathan W. Pillow
• Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights Daniel Soudry, Itay Hubara, Ron Meir
• Incremental Local Gaussian Regression Franziska Meier, Philipp Hennig, Stefan Schaal
• General Table Completion using a Bayesian Nonparametric Model Isabel Valera, Zoubin Ghahramani
• Universal Option Models hengshuai yao, Csaba Szepesvari, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar
• Approximating Hierarchical MV-sets for Hierarchical Clustering Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch
• Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings Ian En-Hsu Yen, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm Deanna Needell, Rachel Ward, Nati Srebro
• A Framework for Testing Identifiability of Bayesian Models of Perception Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar
• Optimistic Planning in Markov Decision Processes Using a Generative Model Balázs Szörényi, Gunnar Kedenburg, Remi Munos
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• The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim, Cynthia Rudin, Julie A. Shah
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• RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning Marek Petrik, Dharmashankar Subramanian
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• On the relations of LFPs & Neural Spike Trains David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
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• Self-Paced Learning with Diversity Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander Hauptmann
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• Searching for Higgs Boson Decay Modes with Deep Learning Peter J. Sadowski, Daniel Whiteson, Pierre Baldi
• Structure Regularization for Structured Prediction Xu Sun
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• An Accelerated Proximal Coordinate Gradient Method Qihang Lin, Zhaosong Lu, Lin Xiao
• Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
• Simple MAP Inference via Low-Rank Relaxations Roy Frostig, Sida Wang, Percy S. Liang, Christopher D. Manning
• A* Sampling Chris J. Maddison, Daniel Tarlow, Tom Minka
• A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
• Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V. Le
• Improved Distributed Principal Component Analysis Yingyu Liang, Maria-Florina F. Balcan, Vandana Kanchanapally, David Woodruff
• Sparse Polynomial Learning and Graph Sketching Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam Klivans
• Tight Continuous Relaxation of the Balanced k-Cut Problem Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
• Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
• Expectation-Maximization for Learning Determinantal Point Processes Jennifer A. Gillenwater, Alex Kulesza, Emily Fox, Ben Taskar
• Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs David I. Inouye, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Streaming, Memory Limited Algorithms for Community Detection Se-Young Yun, marc lelarge, Alexandre Proutiere
• Content-based recommendations with Poisson factorization Prem K. Gopalan, Laurent Charlin, David Blei
• A Statistical Decision-Theoretic Framework for Social Choice Hossein Azari Soufiani, David C. Parkes, Lirong Xia
• Compressive Sensing of Signals from a GMM with Sparse Precision Matrices Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin
• Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven
• On Sparse Gaussian Chain Graph Models Calvin McCarter, Seyoung Kim
• Orbit Regularization Renato Negrinho, Andre Martins
• Efficient Minimax Strategies for Square Loss Games Wouter M. Koolen, Alan Malek, Peter L. Bartlett
• A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs Miles Lopes
• Large-Margin Convex Polytope Machine Alex Kantchelian, Michael C. Tschantz, Ling Huang, Peter L. Bartlett, Anthony D. Joseph, J. D. Tygar
• Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Yarin Gal, Mark van der Wilk, Carl Edward Rasmussen
• Learning Distributed Representations for Structured Output Prediction Vivek Srikumar, Christopher D. Manning
• Convex Deep Learning via Normalized Kernels Özlem Aslan, Xinhua Zhang, Dale Schuurmans
• Tight convex relaxations for sparse matrix factorization Emile Richard, Guillaume R. Obozinski, Jean-Philippe Vert
• Learning to Search in Branch and Bound Algorithms He He, Hal Daume III, Jason M. Eisner
• An Integer Polynomial Programming Based Framework for Lifted MAP Inference Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav G. Gogate
• Conditional Random Field Autoencoders for Unsupervised Structured Prediction Waleed Ammar, Chris Dyer, Noah A. Smith
• How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson
• Accelerated Mini-batch Randomized Block Coordinate Descent Method Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
• A Latent Source Model for Online Collaborative Filtering Guy Bresler, George H. Chen, Devavrat Shah
• Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
• Learning with Pseudo-Ensembles Philip Bachman, Ouais Alsharif, Doina Precup
• Learning Time-Varying Coverage Functions Nan Du, Yingyu Liang, Maria-Florina F. Balcan, Le Song
• Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time Zhaoran Wang, Huanran Lu, Han Liu
• Discriminative Metric Learning by Neighborhood Gerrymandering Shubhendu Trivedi, David Mcallester, Greg Shakhnarovich
• Finding a sparse vector in a subspace: Linear sparsity using alternating directions Qing Qu, Ju Sun, John Wright
• Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh
• Discrete Graph Hashing Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang
• Feedback Detection for Live Predictors Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi
• Rates of Convergence for Nearest Neighbor Classification Kamalika Chaudhuri, Sanjoy Dasgupta
• Consistency of weighted majority votes Daniel Berend, Aryeh Kontorovich
• Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling Mingyuan Zhou
• Zero-shot recognition with unreliable attributes Dinesh Jayaraman, Kristen Grauman
• Concavity of reweighted Kikuchi approximation Po-Ling Loh, Andre Wibisono
• Online Decision-Making in General Combinatorial Spaces Arun Rajkumar, Shivani Agarwal
• Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning Mohammad Taha Bahadori, Qi (Rose) Yu, Yan Liu
• Clustered factor analysis of multineuronal spike data Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
• Algorithms for CVaR Optimization in MDPs Yinlam Chow, Mohammad Ghavamzadeh
• Factoring Variations in Natural Images with Deep Gaussian Mixture Models Aaron van den Oord, Benjamin Schrauwen
• Partition-wise Linear Models Hidekazu Oiwa, Ryohei Fujimaki
• LSDA: Large Scale Detection through Adaptation Judy Hoffman, Sergio Guadarrama, Eric S. Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko
• Deep Networks with Internal Selective Attention through Feedback Connections Marijn F. Stollenga, Jonathan Masci, Faustino Gomez, Juergen Schmidhuber
• Parallel Feature Selection Inspired by Group Testing Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, XuanLong Nguyen, Christopher Ré, Venu Govindaraju
• Low-Rank Time-Frequency Synthesis Cédric Févotte, Matthieu Kowalski
• Pre-training of Recurrent Neural Networks via Linear Autoencoders Luca Pasa, Alessandro Sperduti
• Semi-supervised Learning with Deep Generative Models Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
• Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation Mingjun Zhong, Nigel Goddard, Charles Sutton
• Stochastic variational inference for hidden Markov models Nicholas Foti, Jason Xu, Dillon Laird, Emily Fox
• A Wild Bootstrap for Degenerate Kernel Tests Kacper P. Chwialkowski, Dino Sejdinovic, Arthur Gretton
• Biclustering Using Message Passing Luke O'Connor, Soheil Feizi
• Fast Kernel Learning for Multidimensional Pattern Extrapolation Andrew Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai
• Learning on graphs using Orthonormal Representation is Statistically Consistent Rakesh Shivanna, Chiranjib Bhattacharyya
• Spectral k-Support Norm Regularization Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
• Unsupervised learning of an efficient short-term memory network Pietro Vertechi, Wieland Brendel, Christian K. Machens
• Quantized Estimation of Gaussian Sequence Models in Euclidean Balls Yuancheng Zhu, John Lafferty
• Learning a Concept Hierarchy from Multi-labeled Documents Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik, Jonathan Chang
• Variational Gaussian Process State-Space Models Roger Frigola, Yutian Chen, Carl Edward Rasmussen
• Fast Prediction for Large-Scale Kernel Machines Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon

Advances in Neural Information Processing Systems 28 (NIPS 2015)

The papers below appear in Advances in Neural Information Processing Systems 28 edited by  C. Cortes  and  N.D. Lawrence  and  D.D. Lee  and  M. Sugiyama  and  R. Garnett .
They are proceedings from the conference, "Neural Information Processing Systems 2015."

• Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar Bhadresh Shah, Denny Zhou
• Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, Aditya Menon, Robert C. Williamson
• Algorithmic Stability and Uniform Generalization Ibrahim M. Alabdulmohsin
• Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models Theodoros Tsiligkaridis, Theodoros Tsiligkaridis, Keith Forsythe
• Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, Amos J. Storkey
• Robust Portfolio Optimization Huitong Qiu, Fang Han, Han Liu, Brian Caffo
• Logarithmic Time Online Multiclass prediction Anna E. Choromanska, John Langford
• Planar Ultrametrics for Image Segmentation Julian E. Yarkony, Charless Fowlkes
• Expressing an Image Stream with a Sequence of Natural Sentences Cesc C. Park, Gunhee Kim
• Parallel Correlation Clustering on Big Graphs Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael I. Jordan
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
• Space-Time Local Embeddings Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet
• A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear MeasurementsQinqing Zheng, John Lafferty
• Smooth Interactive Submodular Set Cover Bryan D. He, Yisong Yue
• Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Josh Tenenbaum
• On the Pseudo-Dimension of Nearly Optimal Auctions Jamie H. Morgenstern, Tim Roughgarden
• Unlocking neural population non-stationarities using hierarchical dynamics models Mijung Park, Gergo Bohner, Jakob H. Macke
• Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM) Mijung Park, Wittawat Jitkrittum, Ahmad Qamar, Zoltan Szabo, Lars Buesing, Maneesh Sahani
• Color Constancy by Learning to Predict Chromaticity from Luminance Ayan Chakrabarti
• Fast and Accurate Inference of Plackett–Luce Models Lucas Maystre, Matthias Grossglauser
• Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig
• Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets Armand Joulin, Tomas Mikolov
• Where are they looking? Adria Recasens, Aditya Khosla, Carl Vondrick, Antonio Torralba
• The Pareto Regret Frontier for Bandits Tor Lattimore
• On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors Andrea Montanari, Daniel Reichman, Ofer Zeitouni
• Measuring Sample Quality with Stein's Method Jackson Gorham, Lester Mackey
• Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution Yan Huang, Wei Wang, Liang Wang
• Bounding errors of Expectation-Propagation Guillaume P. Dehaene, Simon Barthelmé
• A fast, universal algorithm to learn parametric nonlinear embeddings Miguel A. Carreira-Perpinan, Max Vladymyrov
• Texture Synthesis Using Convolutional Neural Networks Leon Gatys, Alexander S. Ecker, Matthias Bethge
• Extending Gossip Algorithms to Distributed Estimation of U-statistics Igor Colin, Aurélien Bellet, Joseph Salmon, Stéphan Clémençon
• Streaming, Distributed Variational Inference for Bayesian Nonparametrics Trevor Campbell, Julian Straub, John W. Fisher III, Jonathan P. How
• Learning visual biases from human imagination Carl Vondrick, Hamed Pirsiavash, Aude Oliva, Antonio Torralba
• Smooth and Strong: MAP Inference with Linear Convergence Ofer Meshi, Mehrdad Mahdavi, Alex Schwing
• Copeland Dueling Bandits Masrour Zoghi, Zohar S. Karnin, Shimon Whiteson, Maarten de Rijke
• Optimal Ridge Detection using Coverage Risk Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman
• Top-k Multiclass SVM Maksim Lapin, Matthias Hein, Bernt Schiele
• Policy Evaluation Using the Ω-Return Philip S. Thomas, Scott Niekum, Georgios Theocharous, George Konidaris
• Orthogonal NMF through Subspace Exploration Megasthenis Asteris, Dimitris Papailiopoulos, Alexandros G. Dimakis
• Stochastic Online Greedy Learning with Semi-bandit Feedbacks Tian Lin, Jian Li, Wei Chen
• Deeply Learning the Messages in Message Passing Inference Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel
• Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring David Kappel, Stefan Habenschuss, Robert Legenstein, Wolfgang Maass
• Accelerated Proximal Gradient Methods for Nonconvex Programming Huan Li, Zhouchen Lin
• Approximating Sparse PCA from Incomplete Data ABHISEK KUNDU, Petros Drineas, Malik Magdon-Ismail
• Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabas Poczos, Larry Wasserman, james m. robins
• Column Selection via Adaptive Sampling Saurabh Paul, Malik Magdon-Ismail, Petros Drineas
• HONOR: Hybrid Optimization for NOn-convex Regularized problems Pinghua Gong, Jieping Ye
• 3D Object Proposals for Accurate Object Class Detection Xiaozhi Chen, Kaustav Kundu, Yukun Zhu, Andrew G. Berneshawi, Huimin Ma, Sanja Fidler, Raquel Urtasun
• Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits Huasen Wu, R. Srikant, Xin Liu, Chong Jiang
• Tensorizing Neural Networks Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, Dmitry P. Vetrov
• Parallelizing MCMC with Random Partition Trees Xiangyu Wang, Fangjian Guo, Katherine A. Heller, David B. Dunson
• A Reduced-Dimension fMRI Shared Response Model Po-Hsuan (Cameron) Chen, Janice Chen, Yaara Yeshurun, Uri Hasson, James Haxby, Peter J. Ramadge
• Spectral Learning of Large Structured HMMs for Comparative Epigenomics Chicheng Zhang, Jimin Song, Kamalika Chaudhuri, Kevin Chen
• Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability Xia Qu, Prashant Doshi
• Estimating Mixture Models via Mixtures of Polynomials Sida Wang, Arun Tejasvi Chaganty, Percy S. Liang
• On the Global Linear Convergence of Frank-Wolfe Optimization Variants Simon Lacoste-Julien, Martin Jaggi
• Deep Knowledge Tracing Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J. Guibas, Jascha Sohl-Dickstein
• Rethinking LDA: Moment Matching for Discrete ICA Anastasia Podosinnikova, Francis Bach, Simon Lacoste-Julien
• Efficient Compressive Phase Retrieval with Constrained Sensing Vectors Sohail Bahmani, Justin Romberg
• Barrier Frank-Wolfe for Marginal Inference Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag
• Learning Theory and Algorithms for Forecasting Non-stationary Time Series Vitaly Kuznetsov, Mehryar Mohri
• Compressive spectral embedding: sidestepping the SVD Dinesh Ramasamy, Upamanyu Madhow
• A Nonconvex Optimization Framework for Low Rank Matrix Estimation Tuo Zhao, Zhaoran Wang, Han Liu
• Automatic Variational Inference in Stan Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei
• Attention-Based Models for Speech Recognition Jan K. Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, Kyunghyun Cho, Yoshua Bengio
• Closed-form Estimators for High-dimensional Generalized Linear Models Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar
• Online F-Measure Optimization Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier
• Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier
• M-Best-Diverse Labelings for Submodular Energies and Beyond Alexander Kirillov, Dmytro Shlezinger, Dmitry P. Vetrov, Carsten Rother, Bogdan Savchynskyy
• Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number Janne H. Korhonen, Pekka Parviainen
• Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring Gunwoong Park, Garvesh Raskutti
• Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy Marylou Gabrie, Eric W. Tramel, Florent Krzakala
• Character-level Convolutional Networks for Text Classification Xiang Zhang, Junbo Zhao, Yann LeCun
• Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen
• Black-box optimization of noisy functions with unknown smoothness Jean-Bastien Grill, Michal Valko, Remi Munos, Remi Munos
• Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters Emmanuel Abbe, Colin Sandon
• Deep learning with Elastic Averaging SGD Sixin Zhang, Anna E. Choromanska, Yann LeCun
• Monotone k-Submodular Function Maximization with Size Constraints Naoto Ohsaka, Yuichi Yoshida
• Active Learning from Weak and Strong Labelers Chicheng Zhang, Kamalika Chaudhuri
• On the Optimality of Classifier Chain for Multi-label Classification Weiwei Liu, Ivor Tsang
• Robust Regression via Hard Thresholding Kush Bhatia, Prateek Jain, Purushottam Kar
• Sparse Local Embeddings for Extreme Multi-label Classification Kush Bhatia, Himanshu Jain, Purushottam Kar, Manik Varma, Prateek Jain
• Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems Yuxin Chen, Emmanuel Candes
• A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure Peter Schulam, Suchi Saria
• Subspace Clustering with Irrelevant Features via Robust Dantzig Selector Chao Qu, Huan Xu
• Sparse PCA via Bipartite Matchings Megasthenis Asteris, Dimitris Papailiopoulos, Anastasios Kyrillidis, Alexandros G. Dimakis
• Fast Randomized Kernel Ridge Regression with Statistical Guarantees Ahmed Alaoui, Michael W. Mahoney
• Online Learning for Adversaries with Memory: Price of Past Mistakes Oren Anava, Elad Hazan, Shie Mannor
• Convolutional spike-triggered covariance analysis for neural subunit models Anqi Wu, Il Memming Park, Jonathan W. Pillow
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian SHI, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, Wang-chun WOO
• GAP Safe screening rules for sparse multi-task and multi-class models Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
• Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces Takashi Takenouchi, Takafumi Kanamori
• Statistical Model Criticism using Kernel Two Sample Tests James R. Lloyd, Zoubin Ghahramani
• Precision-Recall-Gain Curves: PR Analysis Done Right Peter Flach, Meelis Kull
• A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice Tasuku Soma, Yuichi Yoshida
• Bidirectional Recurrent Neural Networks as Generative Models Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, Juha T. Karhunen
• Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling Zheng Qu, Peter Richtarik, Tong Zhang
• Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets Justin Domke
• Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks Minhyung Cho, Chandra Dhir, Jaehyung Lee
• Large-scale probabilistic predictors with and without guarantees of validity Vladimir Vovk, Ivan Petej, Valentina Fedorova
• Shepard Convolutional Neural Networks Jimmy SJ Ren, Li Xu, Qiong Yan, Wenxiu Sun
• Matrix Manifold Optimization for Gaussian Mixtures Reshad Hosseini, Suvrit Sra
• Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding Rie Johnson, Tong Zhang
• Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models Akihiro Kishimoto, Radu Marinescu, Adi Botea
• Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling Ming Liang, Xiaolin Hu, Bo Zhang
• Bounding the Cost of Search-Based Lifted Inference David B. Smith, Vibhav G. Gogate
• Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton
• Linear Multi-Resource Allocation with Semi-Bandit Feedback Tor Lattimore, Koby Crammer, Csaba Szepesvari
• Unsupervised Learning by Program Synthesis Kevin Ellis, Armando Solar-Lezama, Josh Tenenbaum
• Enforcing balance allows local supervised learning in spiking recurrent networks Ralph Bourdoukan, Sophie Denève
• Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang, Hsiao-Yu Tung, Alexander J. Smola, Anima Anandkumar
• Differentially private subspace clustering Yining Wang, Yu-Xiang Wang, Aarti Singh
• Predtron: A Family of Online Algorithms for General Prediction Problems Prateek Jain, Nagarajan Natarajan, Ambuj Tewari
• Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization Fredrik D. Johansson, Ankani Chattoraj, Chiranjib Bhattacharyya, Devdatt Dubhashi
• SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk Guillaume Papa, Stéphan Clémençon, Aurélien Bellet
• On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs Wei Cao, Jian Li, Yufei Tao, Zhize Li
• The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions Sebastian Bitzer, Stefan Kiebel
• Fast Classification Rates for High-dimensional Gaussian Generative Models Tianyang Li, Adarsh Prasad, Pradeep K. Ravikumar
• Fast Distributed k-Center Clustering with Outliers on Massive Data Gustavo Malkomes, Matt J. Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley
• Human Memory Search as Initial-Visit Emitting Random Walk Kwang-Sung Jun, Xiaojin Zhu, Timothy T. Rogers, Zhuoran Yang, ming yuan
• Non-convex Statistical Optimization for Sparse Tensor Graphical Model Wei Sun, Zhaoran Wang, Han Liu, Guang Cheng
• Convergence Rates of Active Learning for Maximum Likelihood Estimation Kamalika Chaudhuri, Sham M. Kakade, Praneeth Netrapalli, Sujay Sanghavi
• Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis Jimei Yang, Scott E. Reed, Ming-Hsuan Yang, Honglak Lee
• Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier
• Backpropagation for Energy-Efficient Neuromorphic Computing Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, Dharmendra S. Modha
• Alternating Minimization for Regression Problems with Vector-valued Outputs Prateek Jain, Ambuj Tewari
• Learning both Weights and Connections for Efficient Neural Network Song Han, Jeff Pool, John Tran, William Dally
• Optimal Rates for Random Fourier Features Bharath Sriperumbudur, Zoltan Szabo
• The Population Posterior and Bayesian Modeling on Streams James McInerney, Rajesh Ranganath, David Blei
• Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees François-Xavier Briol, Chris Oates, Mark Girolami, Michael A. Osborne
• Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer
• Unified View of Matrix Completion under General Structural Constraints Suriya Gunasekar, Arindam Banerjee, Joydeep Ghosh
• Efficient Output Kernel Learning for Multiple Tasks Pratik Jawanpuria, Maksim Lapin, Matthias Hein, Bernt Schiele
• Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models Michael C. Hughes, William T. Stephenson, Erik Sudderth
• Variational Consensus Monte Carlo Maxim Rabinovich, Elaine Angelino, Michael I. Jordan
• Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma Murat A. Erdogdu
• Practical and Optimal LSH for Angular Distance Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, Ludwig Schmidt
• Learning to Linearize Under Uncertainty Ross Goroshin, Michael F. Mathieu, Yann LeCun
• Finite-Time Analysis of Projected Langevin Monte Carlo Sebastien Bubeck, Ronen Eldan, Joseph Lehec
• Deep Visual Analogy-Making Scott E. Reed, Yi Zhang, Yuting Zhang, Honglak Lee
• Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation Alaa Saade, Florent Krzakala, Lenka Zdeborová
• Online Learning with Adversarial Delays Kent Quanrud, Daniel Khashabi
• Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection Jie Wang, Jieping Ye
• Minimum Weight Perfect Matching via Blossom Belief Propagation Sung-Soo Ahn, Sejun Park, Michael Chertkov, Jinwoo Shin
• Efficient Thompson Sampling for Online Matrix-Factorization Recommendation Jaya Kawale, Hung H. Bui, Branislav Kveton, Long Tran-Thanh, Sanjay Chawla
• Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems Ruoyu Sun, Mingyi Hong
• Lifted Symmetry Detection and Breaking for MAP Inference Timothy Kopp, Parag Singla, Henry Kautz
• Evaluating the statistical significance of biclusters Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
• Discriminative Robust Transformation Learning Jiaji Huang, Qiang Qiu, Guillermo Sapiro, Robert Calderbank
• Bandits with Unobserved Confounders: A Causal Approach Elias Bareinboim, Andrew Forney, Judea Pearl
• Scalable Semi-Supervised Aggregation of Classifiers Akshay Balsubramani, Yoav Freund
• Online Learning with Gaussian Payoffs and Side Observations Yifan Wu, András György, Csaba Szepesvari
• Private Graphon Estimation for Sparse Graphs Christian Borgs, Jennifer Chayes, Adam Smith
• SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals Qing Sun, Dhruv Batra
• Fast Second Order Stochastic Backpropagation for Variational Inference Kai Fan, Ziteng Wang, Jeff Beck, James Kwok, Katherine A. Heller
• Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition Cameron Musco, Christopher Musco
• Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada
• Scalable Inference for Gaussian Process Models with Black-Box Likelihoods Amir Dezfouli, Edwin V. Bonilla
• Fast Bidirectional Probability Estimation in Markov Models Siddhartha Banerjee, Peter Lofgren
• Probabilistic Variational Bounds for Graphical Models Qiang Liu, John W. Fisher III, Alexander T. Ihler
• Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes Ryan J. Giordano, Tamara Broderick, Michael I. Jordan
• Combinatorial Cascading Bandits Branislav Kveton, Zheng Wen, Azin Ashkan, Csaba Szepesvari
• Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path Daniel J. Hsu, Aryeh Kontorovich, Csaba Szepesvari
• Policy Gradient for Coherent Risk Measures Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh, Shie Mannor
• Fast Rates for Exp-concave Empirical Risk Minimization Tomer Koren, Kfir Levy
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks Emily L. Denton, Soumith Chintala, arthur szlam, Rob Fergus
• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Seunghoon Hong, Hyeonwoo Noh, Bohyung Han
• Equilibrated adaptive learning rates for non-convex optimization Yann Dauphin, Harm de Vries, Yoshua Bengio
• BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meins