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Estimating Training Data Influence by Tracking Gradient Descent., , , and . CoRR, (2020)Preface: Conference on Learning Theory (COLT), 2017., and . COLT, volume 65 of Proceedings of Machine Learning Research, page 1-3. PMLR, (2017)Stochastic Negative Mining for Learning with Large Output Spaces., , , , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1940-1949. PMLR, (2019)A simple multi-armed bandit algorithm with optimal variation-bounded regret., and . COLT, volume 19 of JMLR Proceedings, page 817-820. JMLR.org, (2011)Learning rotations with little regret., , and . Mach. Learn., 104 (1): 129-148 (2016)Logarithmic regret algorithms for online convex optimization., , and . Mach. Learn., 69 (2-3): 169-192 (2007)Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization., and . J. Mach. Learn. Res., 15 (1): 2489-2512 (2014)Efficient aggregation algorithms for probabilistic data., , and . SODA, page 346-355. SIAM, (2007)On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data., , , , , and . CoRR, (2022)Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria., and . NIPS, page 625-632. Curran Associates, Inc., (2007)