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Computer Science > Machine Learning

arXiv:2405.20933 (cs)
[Submitted on 31 May 2024]

Title:Concentration Bounds for Optimized Certainty Equivalent Risk Estimation

Authors:Ayon Ghosh, L.A. Prashanth, Krishna Jagannathan
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Abstract:We consider the problem of estimating the Optimized Certainty Equivalent (OCE) risk from independent and identically distributed (i.i.d.) samples. For the classic sample average approximation (SAA) of OCE, we derive mean-squared error as well as concentration bounds (assuming sub-Gaussianity). Further, we analyze an efficient stochastic approximation-based OCE estimator, and derive finite sample bounds for the same. To show the applicability of our bounds, we consider a risk-aware bandit problem, with OCE as the risk. For this problem, we derive bound on the probability of mis-identification. Finally, we conduct numerical experiments to validate the theoretical findings.
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2405.20933 [cs.LG]
  (or arXiv:2405.20933v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2405.20933

Submission history

From: L.A. Prashanth [view email]
[v1] Fri, 31 May 2024 15:32:43 UTC (367 KB)
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