June 10, 2024, 4:44 a.m. | Nikolaos Tsilivis, Natalie Frank, Nathan Srebro, Julia Kempe

stat.ML updates on arXiv.org arxiv.org

arXiv:2406.04981v1 Announce Type: cross
Abstract: We study the implicit bias of optimization in robust empirical risk minimization (robust ERM) and its connection with robust generalization. In classification settings under adversarial perturbations with linear models, we study what type of regularization should ideally be applied for a given perturbation set to improve (robust) generalization. We then show that the implicit bias of optimization in robust ERM can significantly affect the robustness of the model and identify two ways this can happen; …

abstract adversarial arxiv bias classification cs.lg erm linear optimization price regularization risk robust set stat.ml study type

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