Conditionally Risk-Averse Contextual Bandits
Machine Learning
2023-07-11 v2 Machine Learning
Abstract
Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average performance. Designing a risk-averse contextual bandit is challenging because exploration is necessary but risk-aversion is sensitive to the entire distribution of rewards; nonetheless we exhibit the first risk-averse contextual bandit algorithm with an online regret guarantee. We conduct experiments from diverse scenarios where worst-case outcomes should be avoided, from dynamic pricing, inventory management, and self-tuning software; including a production exascale data processing system.
Cite
@article{arxiv.2210.13573,
title = {Conditionally Risk-Averse Contextual Bandits},
author = {Mónika Farsang and Paul Mineiro and Wangda Zhang},
journal= {arXiv preprint arXiv:2210.13573},
year = {2023}
}