Learning under Distribution Mismatch and Model Misspecification
Learning under Distribution Mismatch and Model Misspecification
复制标题
分布不匹配和模型错误指定下的学习
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. Aref
中科院分区：
文献类型：
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作者：
Mohammad Saeed Masiha;A. Gohari;M. Yassaee;M. Aref
我们研究算法在学习算法的训练数据和测试数据集之间存在不匹配。速率延伸理论，它可以利用速率理论的界限来推导概括误差的新界限，反之亦然基于费用的界限严格改善了XU和Raginsky的早期界限，即使没有不匹配，我们也可以讨论“辅助损失功能”如何利用该论文的完整版本。可以在[1]上访问。
We study learning algorithms when there is a mismatch between the distributions of the training and test datasets of a learning algorithm. The effect of this mismatch on the generalization error and model misspecification are quantified. Moreover, we provide a connection between the generalization error and the rate-distortion theory, which allows one to utilize bounds from the rate-distortion theory to derive new bounds on the generalization error and vice versa. In particular, the rate-distortion-based bound strictly improves over the earlier bound by Xu and Raginsky even when there is no mismatch. We also discuss how “auxiliary loss functions” can be utilized to obtain upper bounds on the generalization error. A full version of this paper is accessible at [1].
基于 Wasserstein 距离的泛化信息论观点
DOI：
10.1109/isit.2019.8849359
发表时间：
2019
期刊：
2019 IEEE International Symposium on Information Theory (ISIT
影响因子：
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作者：
Wang, Hao;Diaz, Mario;Santos Filho, Jose Candido;Calmon, Flavio P.
通讯作者：
Calmon, Flavio P.