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On the Convergence of Adam and Beyond.

, , и . ICLR, OpenReview.net, (2018)

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Devika Kale

Rasim Kale

Vidyadhar Vinayak Kalè

Pieter Rihi Kale

Christoph Kaleta

 

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Stochastic Negative Mining for Learning with Large Output Spaces., , , , , и . AISTATS, том 89 из Proceedings of Machine Learning Research, стр. 1940-1949. PMLR, (2019)A simple multi-armed bandit algorithm with optimal variation-bounded regret., и . COLT, том 19 из JMLR Proceedings, стр. 817-820. JMLR.org, (2011)Preface: Conference on Learning Theory (COLT), 2017., и . COLT, том 65 из Proceedings of Machine Learning Research, стр. 1-3. PMLR, (2017)Estimating Training Data Influence by Tracking Gradient Descent., , , и . CoRR, (2020)Learning rotations with little regret., , и . Mach. Learn., 104 (1): 129-148 (2016)Logarithmic regret algorithms for online convex optimization., , и . Mach. Learn., 69 (2-3): 169-192 (2007)On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data., , , , , и . CoRR, (2022)Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization., и . J. Mach. Learn. Res., 15 (1): 2489-2512 (2014)Agnostic Learnability of Halfspaces via Logistic Loss., , , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 10068-10103. PMLR, (2022)Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria., и . NIPS, стр. 625-632. Curran Associates, Inc., (2007)