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Best-of-Both-Worlds Algorithms for Partial Monitoring.

, , and . ALT, volume 201 of Proceedings of Machine Learning Research, page 1484-1515. PMLR, (2023)

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Adaptive Learning Rate for Follow-the-Regularized-Leader: Competitive Analysis and Best-of-Both-Worlds., , and . CoRR, (2024)Best-of-Both-Worlds Algorithms for Partial Monitoring., , and . ALT, volume 201 of Proceedings of Machine Learning Research, page 1484-1515. PMLR, (2023)Large-Scale Price Optimization via Network Flow., and . NIPS, page 3855-3863. (2016)Tracking Regret Bounds for Online Submodular Optimization., , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 3421-3429. PMLR, (2021)Revisiting Online Submodular Minimization: Gap-Dependent Regret Bounds, Best of Both Worlds and Adversarial Robustness.. ICML, volume 162 of Proceedings of Machine Learning Research, page 9678-9694. PMLR, (2022)Best-of-Both-Worlds Linear Contextual Bandits., and . CoRR, (2023)Fast Rates in Online Convex Optimization by Exploiting the Curvature of Feasible Sets., and . CoRR, (2024)Orthogonal polynomial approach to estimation of poles of rational functions from data on open curves., , , and . J. Comput. Appl. Math., (2015)An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits., and . ICDM, page 1318-1323. IEEE, (2019)Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions., , , , , , and . AAAI, page 9791-9798. AAAI Press, (2021)