Model Agnostic Sample Reweighting for Out-of-Distribution Learning

• URL: http://arxiv.org/abs/2301.09819v1
• Date: Tue, 24 Jan 2023 05:11:03 GMT
• Title: Model Agnostic Sample Reweighting for Out-of-Distribution Learning
• Authors: Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang
• Abstract summary: We propose a principled method, textbfAgnostic samtextbfPLe rtextbfEweighting (textbfMAPLE) to effectively address OOD problem. Our key idea is to find an effective reweighting of the training samples so that the standard empirical risk minimization training of a large model leads to superior OOD generalization performance.
• Score: 38.843552982739354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributionally robust optimization (DRO) and invariant risk minimization (IRM) are two popular methods proposed to improve out-of-distribution (OOD) generalization performance of machine learning models. While effective for small models, it has been observed that these methods can be vulnerable to overfitting with large overparameterized models. This work proposes a principled method, \textbf{M}odel \textbf{A}gnostic sam\textbf{PL}e r\textbf{E}weighting (\textbf{MAPLE}), to effectively address OOD problem, especially in overparameterized scenarios. Our key idea is to find an effective reweighting of the training samples so that the standard empirical risk minimization training of a large model on the weighted training data leads to superior OOD generalization performance. The overfitting issue is addressed by considering a bilevel formulation to search for the sample reweighting, in which the generalization complexity depends on the search space of sample weights instead of the model size. We present theoretical analysis in linear case to prove the insensitivity of MAPLE to model size, and empirically verify its superiority in surpassing state-of-the-art methods by a large margin. Code is available at \url{https://github.com/x-zho14/MAPLE}.

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