TY - JOUR AU - Xi, Guangyu AU - Tao, Chao AU - Zhou, Yuan PY - 2021/05/18 Y2 - 2024/03/28 TI - Near-Optimal MNL Bandits Under Risk Criteria JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 12 SE - AAAI Technical Track on Machine Learning V DO - 10.1609/aaai.v35i12.17245 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17245 SP - 10397-10404 AB - 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 business. 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. ER -