iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
题目：学习用于算法决策的个别公平数据表示 论文介绍了一种将用户记录概率映射为低秩表示的方法，该方法在下游应用中协调了个体的公平性和分类器和排名的效用。通过定义一个组合损失函数来学习低秩数据表示，该工作解决了效用和公平之间的基本权衡。作者通过将他们的方法应用于各种真实数据集上的分类和学习到排名任务，证明了他们的方法的通用性。他们的实验表明，与此设置的最佳前期工作相比，有实质性的改进。（低秩：矩阵的秩
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相关文章
- 1. Decision making
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- 3. Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning
- 4. A Vision for Making Deep Learning Simple
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- 9. ICCV 2015 Learning to Track: Online Multi-Object Tracking by Decision Making 阅读笔记
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