Risk-Averse Stochastic Convex Bandit
Machine Learning
2018-10-02 v1 Machine Learning
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.
Cite
@article{arxiv.1810.00737,
title = {Risk-Averse Stochastic Convex Bandit},
author = {Adrian Rivera Cardoso and Huan Xu},
journal= {arXiv preprint arXiv:1810.00737},
year = {2018}
}