Concentration Bounds for Optimized Certainty Equivalent Risk Estimation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 31 May 2024.
Builds on
9 of 25 references.
References · 9
- 2015Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Controlcited by 34
- 2020Learning Bounds for Risk-sensitive Learningcited by 33
- 2019Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewardscited by 19
- 2019A Wasserstein distance approach for concentration of empirical risk estimatescited by 18
- 2013Concentration bounds for temporal difference learning with linear function approximation: The case of batch data and uniform samplingcited by 13
- 2020PAC-Bayesian Bound for the Conditional Value at Riskcited by 13
- 2019Estimation of Spectral Risk Measurescited by 9
- 2021Online Estimation and Optimization of Utility-Based Shortfall Riskcited by 5
- 2023Optimization of utility-based shortfall risk: A non-asymptotic viewpointcited by 2
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Citing papers per year: 2026: 2.
2 papers.