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Analyzing Queueing Problems via Bandits With Linear Reward & Nonlinear Workload Fairness.

, , and . IEEE Trans. Mob. Comput., 23 (4): 3410-3423 (April 2024)

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Optimizing recommendations under abandonment risks: Models and algorithms., , , and . Perform. Evaluation, (September 2023)Achieving Near-Optimal Individual Regret & Low Communications in Multi-Agent Bandits., , , , , , and . ICLR, OpenReview.net, (2023)Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications., , and . IJCAI, page 3537-3543. ijcai.org, (2022)Analyzing Queueing Problems via Bandits With Linear Reward & Nonlinear Workload Fairness., , and . IEEE Trans. Mob. Comput., 23 (4): 3410-3423 (April 2024)Quantum Best Arm Identification., , , , , and . SIGMETRICS Perform. Evaluation Rev., 51 (2): 72-74 (September 2023)Adversarial Attacks on Cooperative Multi-agent Bandits., , , , , , , and . CoRR, (2023)Multi-Fidelity Multi-Armed Bandits Revisited., , , and . CoRR, (2023)Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs., , , , , and . CoRR, (2023)Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms., , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 23181-23212. PMLR, (2022)Exploration for Free: How Does Reward Heterogeneity Improve Regret in Cooperative Multi-agent Bandits?, , , , , , and . UAI, volume 216 of Proceedings of Machine Learning Research, page 2192-2202. PMLR, (2023)