Relative deviation learning bounds and generalization with unbounded loss functions

被引：0
|
作者：
Corinna Cortes
Spencer Greenberg
Mehryar Mohri
机构：
[1] Google Research,
[2] Courant Institute and Google Research,undefined
来源：
Annals of Mathematics and Artificial Intelligence | 2019年 / 85卷
关键词：
Generalization bounds; Learning theory; Unbounded loss functions; Relative deviation bounds; Importance weighting; Unbounded regression; Machine learning; 97R40;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We present an extensive analysis of relative deviation bounds, including detailed proofs of two-sided inequalities and their implications. We also give detailed proofs of two-sided generalization bounds that hold in the general case of unbounded loss functions, under the assumption that a moment of the loss is bounded. We then illustrate how to apply these results in a sample application: the analysis of importance weighting.
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收藏
页码：45 / 70
页数：25
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