Nearly Minimax Optimal Regret for Multinomial Logistic Bandit

In this paper, we study the contextual multinomial logit (MNL) bandit problem in which a learning agent sequentially selects an assortment based on contextual information, and user feedback follows an MNL choice model. There has been a significant discrepancy between lower and upper regret bounds, p...

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Hauptverfasser:	Lee, Joongkyu, Oh, Min-hwan
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
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