Statistically Robust, Risk-Averse Best Arm Identification in Multi-Armed Bandits

Kagrecha, Anmol; Nair, Jayakrishnan; Jagannathan, Krishna

doi:10.1109/tit.2022.3163524

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2022

2024

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Cited by 3 publications

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“…Recent generalizations of this problem replace the expectation objective with other metrics aimed at measuring risk. For example, Bhat and Prashanth (2019); David et al (2018); Galichet et al (2013); Kagrecha et al (2019); Sani et al (2012); Torossian et al (2019); Yu and Nikolova (2013) addressed the MAB problem with a risk-averse (risk-aware) agent.…”

Section: Introductionmentioning

confidence: 99%

Best-Arm Identification Using Extreme Value Theory Estimates of the CVaR

Troop

Godin

2022

JRFM

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We consider a risk-aware multi-armed bandit framework with the goal of avoiding catastrophic risk. Such a framework has multiple applications in financial risk management. We introduce a new conditional value-at-risk (CVaR) estimation procedure combining extreme value theory with automated threshold selection by ordered goodness-of-fit tests, and we apply this procedure to a pure exploration best-arm identification problem under a fixed budget. We empirically compare our results with the commonly used sample average estimator of the CVaR, and we show a significant performance improvement when the underlying arm distributions are heavy-tailed.

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Section: Introductionmentioning

confidence: 99%