Conditionally Risk-Averse Contextual Bandits

Autor:	Farsang, Mónika, Mineiro, Paul, Zhang, Wangda
Rok vydání:	2022
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.13573 Zobrazit plný text záznamu View this record from Arxiv