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LocalNewton: Reducing communication rounds for distributed learning., , , , , и . UAI, том 161 из Proceedings of Machine Learning Research, стр. 632-642. AUAI Press, (2021)Distributed Newton Can Communicate Less and Resist Byzantine Workers., , и . NeurIPS, (2020)Breaking the $T$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits., и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 7531-7549. PMLR, (2022)Multi-agent Heterogeneous Stochastic Linear Bandits., , и . ECML/PKDD (4), том 13716 из Lecture Notes in Computer Science, стр. 300-316. Springer, (2022)Max-affine regression with universal parameter estimation for small-ball designs., , , и . ISIT, стр. 2706-2710. IEEE, (2020)Explore-then-Commit Algorithms for Decentralized Two-Sided Matching Markets., и . ISIT, стр. 2092-2097. IEEE, (2024)Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference., , , и . CoRR, (2022)Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners., , , , и . CoRR, (мая 2025)Multi-robot Cooperative Box-pushing problem using Multi-objective Particle Swarm Optimization Technique, , , , и . CoRR, (2012)Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits., , и . AISTATS, том 130 из Proceedings of Machine Learning Research, стр. 1396-1404. PMLR, (2021)