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Poster
Multinomial Logit Bandit with Low Switching Cost
Kefan Dong · Yingkai Li · Qin Zhang · Yuan Zhou

Thu Jul 16 05:00 PM -- 05:45 PM & Fri Jul 17 04:00 AM -- 04:45 AM (PDT) @
in Poster Session 49 »
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 $O(N \log T)$ assortment switches, almost matching the lower bound $\Omega(\frac{N \log T}{ \log \log T})$. In the fixed-horizon setting, our algorithm FH-DUCB incurs $O(N \log \log T)$ assortment switches, matching the asymptotic lower bound. We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.
Keywords: Online Learning / Bandits · Online Learning, Active Learning, and Bandits

Author Information

Kefan Dong (Tsinghua University)
Yingkai Li (Northwestern University)
Qin Zhang (Indiana University Bloomington)
Yuan Zhou (UIUC)

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