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FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks.

, , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 32304-32330. PMLR, (2023)

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On the Adversarial Robustness of Vision Transformers., , , , and . Trans. Mach. Learn. Res., (2022)Improved dynamic regret for non-degeneracy functions., , , , and . CoRR, (2016)Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning., , , , and . ACL (1), page 2587-2597. Association for Computational Linguistics, (2018)How You Act Tells a Lot: Privacy-Leaking Attack on Deep Reinforcement Learning., , , , , and . AAMAS, page 368-376. International Foundation for Autonomous Agents and Multiagent Systems, (2019)Symmetric Cross Entropy for Robust Learning With Noisy Labels., , , , , and . ICCV, page 322-330. IEEE, (2019)Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion., , , , and . ICML (3), volume 28 of JMLR Workshop and Conference Proceedings, page 1400-1408. JMLR.org, (2013)Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System., , , , , and . KDD, page 1791-1800. ACM, (2021)AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks., , , , , , , and . AAAI, page 742-749. AAAI Press, (2019)Training Meta-Surrogate Model for Transferable Adversarial Attack., , , and . AAAI, page 9516-9524. AAAI Press, (2023)Provably Robust Metric Learning., , , , and . NeurIPS, (2020)