Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control
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Abstract (arXiv)
We study risk-averse decision making, in which an agent selects actions while being uncertain about the true system state. The risk is measured via optimized certainty equivalent (OCE) metrics, which generalize popular criteria such as mean-variance risk and conditional value-at-risk (CVaR). We characterize the optimal policy under known distributions, and show that it reduces to a prediction set-based solution for the CVaR. This provides an operational interpretation of conformal prediction-type prediction sets. For unknown distributions, we develop a data-driven calibration strategy, based on a synthetic model for the likelihood and held-out calibration data, yielding high-probability control of the OCE risk. The approach is evaluated on two wireless beamforming settings.
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References · 9
- 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 271
- 2019Distributionally Robust Optimization: A Reviewcited by 76
- 2023Sionna RT: Differentiable Ray Tracing for Radio Propagation Modelingcited by 62
- 2022A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systemscited by 18
- 2025How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networkscited by 6
- 2026Optimized Certainty Equivalent Risk-Controlling Prediction Setscited by 2
- 2026Statistically Valid Post-Training Hyperparameter Selection: From Tuning to Guaranteescited by 2
- 2026Decision Making Needs Uncertainty Quantification [Lecture Notes]cited by 2
- 2026Optimized Certainty Equivalent Risk Minimization Using Samples: Algorithms, Convergence Rates, and Applicationscited by 1
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