Risk-Averse Stochastic Convex Bandit

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Adrian Rivera Cardoso, Huan Xu ;
Proceedings of Machine Learning Research, PMLR 89:39-47, 2019.

Abstract

Motivated by applications in clinical trials and finance, we study the problem of online convex optimization (with bandit feedback) where the decision maker is risk-averse. We provide two algorithms to solve this problem. The first one is a descent-type algorithm which is easy to implement. The second algorithm, which combines the ellipsoid method and a center point device, achieves (almost) optimal regret bounds with respect to the number of rounds. To the best of our knowledge this is the first attempt to address risk-aversion in the online convex bandit problem.

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