Conditionally Risk-Averse Contextual Bandits

Mónika Farsang; Paul Mineiro; Wangda Zhang

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arXiv · 2210.13573

Conditionally Risk-Averse Contextual Bandits

Mónika Farsang·Paul Mineiro·Wangda Zhang

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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.

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Mónika Farsang, Paul Mineiro, Wangda Zhang. 2022-10-24. Conditionally Risk-Averse Contextual Bandits. https://arxiv.org/abs/2210.13573

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