Multinomial Logit Bandit with Low Switching Cost

Jul 12, 2020

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We study multinomial logit bandit with limited adaptivity, where the algorithms change their exploration actions as infrequently as possible when achieving almost optimal minimax regret. We propose two measures of adaptivity: the assortment switching cost and the more fine-grained item switching cost. We present an anytime algorithm (AT-DUCB) with O(N logT) assortment switches, almost matching the lower bound Ω(N logT/ loglogT). In the fixed-horizon setting, our algorithm FH-DUCB incurs O(N loglogT) assortment switches, matching the asymptotic lower bound. We also present the ESUCB algorithm with item switching cost O(N log^2 T).

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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