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Minibatch vs Local SGD for Heterogeneous Distributed Learning., , and . NeurIPS, (2020)The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication., , , and . COLT, volume 134 of Proceedings of Machine Learning Research, page 4386-4437. PMLR, (2021)Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints., , , , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 1397-1405. PMLR, (2019)Lower Bounds for Non-Convex Stochastic Optimization., , , , , and . CoRR, (2019)Lower bounds for non-convex stochastic optimization., , , , , and . Math. Program., 199 (1): 165-214 (May 2023)A Stochastic Newton Algorithm for Distributed Convex Optimization., , , , and . NeurIPS, page 26818-26830. (2021)Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization., , , , and . NeurIPS, page 8505-8515. (2018)The Gradient Complexity of Linear Regression., , , and . COLT, volume 125 of Proceedings of Machine Learning Research, page 627-647. PMLR, (2020)A Field Guide to Federated Optimization., , , , , , , , , and 43 other author(s). CoRR, (2021)Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis., , , , , and . CoRR, (2019)