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The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization

, , and . (2020)cite arxiv:2002.11080.

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The curious case of adversarially robust models: More data can help, double descend, or hurt generalization., , and . UAI, volume 161 of Proceedings of Machine Learning Research, page 129-139. AUAI Press, (2021)More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models., , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 1670-1680. PMLR, (2020)Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 24785-24811. PMLR, (2023)Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective., , , , , , , , , and . CoRR, (2023)Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning., , , , , and . AISTATS, volume 206 of Proceedings of Machine Learning Research, page 375-407. PMLR, (2023)Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-Aware Contrastive Distillation., , , , and . IPMI, volume 13939 of Lecture Notes in Computer Science, page 641-653. Springer, (2023)Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes., , , and . ICLR, OpenReview.net, (2023)ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast., , , , , and . MICCAI (4), volume 14223 of Lecture Notes in Computer Science, page 194-205. Springer, (2023)Learning Stochastic Shortest Path with Linear Function Approximation., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 15584-15629. PMLR, (2022)Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets., , , , , and . NeurIPS, (2022)