Near-Optimal MNL Bandits Under Risk Criteria

• URL: http://arxiv.org/abs/2009.12511v3
• Date: Tue, 16 Mar 2021 02:22:23 GMT
• Title: Near-Optimal MNL Bandits Under Risk Criteria
• Authors: Guangyu Xi, Chao Tao and Yuan Zhou
• Abstract summary: We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria. We design algorithms for a broad class of risk criteria, including but not limited to the well-known conditional value-at-risk, Sharpe ratio and entropy risk, and prove that they suffer a near-optimal regret.
• Score: 13.251377915797674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study MNL bandits, which is a variant of the traditional multi-armed bandit problem, under risk criteria. Unlike the ordinary expected revenue, risk criteria are more general goals widely used in industries and bussiness. We design algorithms for a broad class of risk criteria, including but not limited to the well-known conditional value-at-risk, Sharpe ratio and entropy risk, and prove that they suffer a near-optimal regret. As a complement, we also conduct experiments with both synthetic and real data to show the empirical performance of our proposed algorithms.

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