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Stochastic Matching Bandits with Rare Optimization Updates
We introduce a bandit framework for stochastic matching under the multinomial logit (MNL) choice model. In this 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 unknown preferences and yield
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