@article{Xi_Tao_Zhou_2021, title={Near-Optimal MNL Bandits Under Risk Criteria}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17245}, DOI={10.1609/aaai.v35i12.17245}, abstractNote={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.}, number={12}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Xi, Guangyu and Tao, Chao and Zhou, Yuan}, year={2021}, month={May}, pages={10397-10404} }