# Conformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control

arXiv: https://arxiv.org/abs/2608.28179 · HTML: https://mlsanity.com/paper/2608.28179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control](https://mlsanity.com/paper/2110.01052/) (2021)
- [Distributionally Robust Optimization: A Review](https://mlsanity.com/paper/1908.05659/) (2019)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks](https://mlsanity.com/paper/2507.07067/) (2025)
- [Optimized Certainty Equivalent Risk-Controlling Prediction Sets](https://mlsanity.com/paper/2602.13660/) (2026)
- [Statistically Valid Post-Training Hyperparameter Selection: From Tuning to Guarantees](https://mlsanity.com/paper/2606.25601/) (2026)
- [Decision Making Needs Uncertainty Quantification [Lecture Notes]](https://mlsanity.com/paper/2607.14407/) (2026)
- [Optimized Certainty Equivalent Risk Minimization Using Samples: Algorithms, Convergence Rates, and Applications](https://mlsanity.com/paper/2608.07113/) (2026)

## 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.