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Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions.

, , , , , , и . AAAI, стр. 9791-9798. AAAI Press, (2021)

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Kei Yoshimoto

Kei Ishii

Kei Müller-Jensen

Kei Wieland Mülller

Kęstutis Daugirdas

 

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Online Task Assignment Problems with Reusable Resources., , , , , , и . AAAI, стр. 5199-5207. AAAI Press, (2022)Contextual Conservative Interleaving Bandits.. ICML, том 202 из Proceedings of Machine Learning Research, стр. 33468-33489. PMLR, (2023)A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits., , , , , , и . AISTATS, том 130 из Proceedings of Machine Learning Research, стр. 3367-3375. PMLR, (2021)An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits., и . ICDM, стр. 1318-1323. IEEE, (2019)Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions., , , , , , и . AAAI, стр. 9791-9798. AAAI Press, (2021)Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback., , , , , , и . NeurIPS, стр. 10589-10598. (2019)Improved Regret Bounds for Bandit Combinatorial Optimization., , , , , , и . NeurIPS, стр. 12027-12036. (2019)Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds., и . COLT, том 195 из Proceedings of Machine Learning Research, стр. 2653-2677. PMLR, (2023)Delay and Cooperation in Nonstochastic Linear Bandits., , , , , , и . NeurIPS, (2020)