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arXiv Machine Learning

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

The paper investigates risk‑averse decision making where an agent chooses actions under uncertainty about the system state, using optimized certainty equivalent (OCE) metrics that encompass mean‑variance risk and CVaR. For known distributions, the optimal policy simplifies to a prediction‑set‑based solution for CVaR, linking it to conformal prediction sets. When distributions are unknown, the authors propose a data‑driven calibration method that employs a synthetic likelihood model and held‑out data to achieve high‑probability OCE risk control, and they demonstrate the method on two wireless beamforming scenarios.

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arXiv Machine Learning
Sep 11

Risk-Averse Decision Making with Multi-Level Reliability Guarantees

The paper investigates how to maximize the weighted average of performance certificates at multiple target outage levels under uncertainty about the system state. It shows that this problem is equivalent to optimizing over nested prediction sets, linking it to conformal prediction and extending prior work on single-level risk-averse decision making. A dual formulation is derived that decouples the optimization across input values, and numerical experiments on a diversity-based wireless transmission system illustrate the trade-offs involved in enforcing multi-level certificates with a single shared policy.

By Amirmohammad Farzaneh, Osvaldo Simeone
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arXiv Machine Learning
Aug 28

TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction

The paper introduces TRACE-CRC, a trajectory‑adaptive conformal risk control method for multi‑step channel state information (CSI) prediction. It builds Frobenius‑norm uncertainty balls around predicted CSI matrices and controls the risk that any future frame is uncovered, using future‑step‑dependent error profiling, trajectory difficulty stratification, and learn‑then‑test risk control. Experiments show that TRACE‑CRC delivers reliable trajectory‑level coverage with smaller uncertainty balls than conservative multi‑step corrections and avoids undercoverage seen in stepwise and adaptive baselines.

By Kiarash Rezaei, Mehdi Sattari, Javad Aliakbari, Tommy Svensson, Paolo Monti, Carlos Natalino
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arXiv Machine Learning
Jun 11

Calibrating Decision Robustness via Inverse Conformal Risk Control

arXiv:2510. 07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions.

By Wenbin Zhou, Shixiang Zhu
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arXiv Machine Learning
Jul 9

Optimal Conformal Prediction under Epistemic Uncertainty

arXiv:2505. 19033v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees.

By Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies, Willem Waegeman, Aleksandar Bojchevski, Eyke H\"ullermeier
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arXiv Machine Learning
Jul 21

Isotonic Conformal Prediction

arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.

By Daniel Bensimon, Sean Xiang Yu, Eric D. Kolaczyk, Archer Y. Yang
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arXiv Machine Learning
Sep 22

Conformal Robustness in Prediction-Driven Decision-Making

The paper introduces a score‑calibrated robustness framework that transforms any fixed point predictor into a decision‑relevant uncertainty representation using distribution‑free conformal calibration. By employing the conformal score as the core unit of robustness, the authors derive both reliability‑based robust optimization and target‑oriented Conformal Robust Satisficing formulations, linking them through a shared robust decision frontier and a fragility measure. Experiments on synthetic data and a real online‑grocery inventory case study demonstrate the framework’s ability to improve reliability, reduce costs, and provide interpretable uncertainty scales for black‑box predictors.

By Lingjie Zhao, Hansheng Jiang, Wei Qi
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