Title: Batched Stochastic Matching Bandits
Primary area: learning theory
Keywords: Bandits, Matching

Abstract: In this study, we introduce a novel bandit framework for stochastic matching based on the Multi-nomial Logit (MNL) choice model.  In our setting,  $N$ agents on one side are assigned to $K$ arms on the other side, where each arm stochastically selects an agent from its assigned pool according to an unknown preference and yields a corresponding reward.  The objective is to minimize regret by maximizing the cumulative revenue from successful matches across all agents. This task requires solving a combinatorial optimization problem based on estimated preferences, which is NP-hard and leads a naive approach to incur a computational cost of $O(K^N)$ per round. To address this challenge, we propose batched algorithms that limit the frequency of matching updates, thereby reducing the amortized computational cost—i.e., the average cost per round—to $O(1)$ while still achieving a regret bound of $\tilde{O}(\sqrt{T})$.