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cs.LG2026
Expected Sample Complexity in Multi-Armed Bandits
Nadav Sukenik, Nadav Merlis
Sample complexity is a widely used metric in sequential decision-making problems, defined as the number of suboptimal decisions during the interaction between the agent and an envi…
cs.LG2026
Adaptive Bandit Algorithms for Contextual Matching Markets
Shiyun Lin, Simon Mauras, Vianney Perchet +1
We study bandit learning in matching markets, where players and arms constitute the two market sides, and the players' utilities are linear in the arm contexts. In each round, new…
cs.LG2026
Reinforcement Learning with Multi-Step Lookahead Information Via Adaptive Batching
Nadav Merlis
We study tabular reinforcement learning problems with multiple steps of lookahead information. Before acting, the learner observes steps of future transition and reward real…