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This research introduces a distributionally robust learning framework that automates the tuning of robustness mechanisms via bilevel optimization, eliminating the need for extensive manual grid search. Theoretically, it achieves generalization guarantees comparable to exhaustive methods while offering superior computational efficiency, particularly when handling simultaneous intra-group and inter-group distribution shifts. By learning these parameters from held-out data, the approach significantly enhances model scalability and adaptability in dynamic environments.
Learning the Robustness Mechanism with Bilevel Optimization
arXiv:2609.35541v1 Announce Type: new Abstract: We propose a distributionally robust learning framework where parameters defining the robustness mechanism are learned from held-out data instead of extensively tuned. Using bilevel optimization with both upper and lower level minimax problems, we create two instances of our framework to tackle setups with and without group labels in the training set. Theoretically, we provide sample complexity analysis for our robustness mechanism learning paradigm, showing that it achieves generalization guarantees comparable to exhaustive grid search while being more computationally efficient. Empirically, we evaluate our framework under a challenging setup when both intra-group and inter-group test distribution shifts occur at the same time, thereby demonstrating the efficacy and scalability of our method.
Abstract:We propose a distributionally robust learning framework where parameters defining the robustness mechanism are learned from held-out data instead of extensively tuned. Using bilevel optimization with both upper and lower level minimax problems, we create two instances of our framework to tackle setups with and without group labels in the training set. Theoretically, we provide sample complexity analysis for our robustness mechanism learning paradigm, showing that it achieves generalization guarantees comparable to exhaustive grid search while being more computationally efficient. Empirically, we evaluate our framework under a challenging setup when both intra-group and inter-group test distribution shifts occur at the same time, thereby demonstrating the efficacy and scalability of our method.
Machine Learning (cs.LG)
arXiv:2609.35541 [cs.LG]
(or arXiv:2609.35541v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.35541 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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