iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
題目：學習用於算法決策的個別公平數據表示 論文介紹了一種將用戶記錄概率映射爲低秩表示的方法，該方法在下游應用中協調了個體的公平性和分類器和排名的效用。通過定義一個組合損失函數來學習低秩數據表示，該工作解決了效用和公平之間的基本權衡。作者通過將他們的方法應用於各種真實數據集上的分類和學習到排名任務，證明了他們的方法的通用性。他們的實驗表明，與此設置的最佳前期工作相比，有實質性的改進。（低秩：矩陣的秩
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相關文章
- 1. Decision making
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