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SGD and Hogwild! Convergence Without the Bounded Gradients Assumption, , , , , и . (2018)cite arxiv:1802.03801.Matrix Completion Under Interval Uncertainty: Highlights., , и . ECML/PKDD (3), том 11053 из Lecture Notes in Computer Science, стр. 621-625. Springer, (2018)Optimal diagnostic tests for sporadic Creutzfeldt-Jakob disease based on support vector machine classification of RT-QuIC data, , , и . CoRR, (2012)Coordinate Descent Faceoff: Primal or Dual?, и . ALT, том 83 из Proceedings of Machine Learning Research, стр. 246-267. PMLR, (2018)Federated Learning is Better with Non-Homomorphic Encryption., , , и . DistributedML@CoNEXT, стр. 49-84. ACM, (2023)SGD with Arbitrary Sampling: General Analysis and Improved Rates., , , , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 5200-5209. PMLR, (2019)Local SGD: Unified Theory and New Efficient Methods., , и . AISTATS, том 130 из Proceedings of Machine Learning Research, стр. 3556-3564. PMLR, (2021)An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints., , , и . AISTATS, том 151 из Proceedings of Machine Learning Research, стр. 4482-4498. PMLR, (2022)Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning., , , и . AISTATS, том 151 из Proceedings of Machine Learning Research, стр. 680-720. PMLR, (2022)FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning., , , и . AISTATS, том 151 из Proceedings of Machine Learning Research, стр. 11374-11421. PMLR, (2022)