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Competing Bandits in Non-Stationary Matching Markets., , , , and . IEEE Trans. Inf. Theory, 70 (4): 2831-2850 (April 2024)Max-affine regression with universal parameter estimation for small-ball designs., , , and . ISIT, page 2706-2710. IEEE, (2020)Integral Value Transformations: A Class of Affine Discrete Dynamical Systems and an Application, , , , and . CoRR, (2011)Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation., , , and . CoRR, (2019)An Improved Algorithm for Clustered Federated Learning., , and . CoRR, (2022)Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference., , , and . CoRR, (2022)Model Selection with Near Optimal Rates for Reinforcement Learning with General Model Classes., , and . CoRR, (2021)Distributed Newton Can Communicate Less and Resist Byzantine Workers., , and . NeurIPS, (2020)Breaking the $T$ Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits., and . ICML, volume 162 of Proceedings of Machine Learning Research, page 7531-7549. PMLR, (2022)Multi-agent Heterogeneous Stochastic Linear Bandits., , and . ECML/PKDD (4), volume 13716 of Lecture Notes in Computer Science, page 300-316. Springer, (2022)