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Lixin Shen

2 accepted papers

2025

Revisiting Large-Scale Non-convex Distributionally Robust Optimization

ICLR 2025poster

Distributionally robust optimization (DRO) is a powerful technique to train robust machine learning models that perform well under distribution shifts. Compared with empirical risk minimization (ERM), DRO optimizes the expected loss under the worst-case distribution in an uncertainty set of distribu…

Cited by 0SourcePDFScholar
2024

Large-Scale Non-convex Stochastic Constrained Distributionally Robust Optimization

AAAI 2024technical

Distributionally robust optimization (DRO) is a powerful framework for training robust models against data distribution shifts. This paper focuses on constrained DRO, which has an explicit characterization of the robustness level. Existing studies on constrained DRO mostly focus on convex loss func…

Cited by 5SourcePDFScholar