Near-Optimal MNL Bandits Under Risk Criteria
Keywords:Learning Theory, Online Learning & Bandits
AbstractWe 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.
How to Cite
Xi, G., Tao, C., & Zhou, Y. (2021). Near-Optimal MNL Bandits Under Risk Criteria. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 10397-10404. https://doi.org/10.1609/aaai.v35i12.17245
AAAI Technical Track on Machine Learning V