arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1809.08700cs.LGcs.GTstat.ML
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无嫉妒分类

Envy-Free Classification

Carnegie Mellon University(卡内基梅隆大学);University of Pennsylvania(宾夕法尼亚大学);Harvard University(哈佛大学)

Maria-Florina Balcan, Travis Dick, Ritesh Noothigattu, Ariel D. Procaccia

摘要:

在经典的公平分配问题中,如切蛋糕和分房租,无嫉妒要求每个人(微弱地)喜欢自己的分配。在概念层面上,我们认为,嫉妒自由也提供了一个令人信服的公平分类任务的概念。我们的技术重点是无嫉妒分类的普遍性,即,理解在样本上无嫉妒的分类器是否相对于具有高概率的底层分布几乎无嫉妒。我们的主要结果建立了一个小样本是足够的,以实现这样的保证,当有问题的分类器是属于一个家庭的低Natarajan维的确定性分类器的混合物。

英文摘要:

In classic fair division problems such as cake cutting and rent division, envy-freeness requires that each individual (weakly) prefer his allocation to anyone else's. On a conceptual level, we argue that envy-freeness also provides a compelling notion of fairness for classification tasks. Our technical focus is the generalizability of envy-free classification, i.e., understanding whether a classifier that is envy free on a sample would be almost envy free with respect to the underlying distribution with high probability. Our main result establishes that a small sample is sufficient to achieve such guarantees, when the classifier in question is a mixture of deterministic classifiers that belong to a family of low Natarajan dimension.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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