arXiv Machine Learning July 17, 2026 By Yang Liu, Yuhao Liu, Yunran Wei

A Noise-Robust Elicit-to-Optimize Framework for Distortion Riskmetrics via Inverse Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2607. 14373v1 Announce Type: new Abstract: We propose a noise-robust elicit-to-optimize framework that integrates inverse reinforcement learning (IRL) and reinforcement learning (RL) for eliciting agents' risk preferences and optimizing policies under a broad class of risk objectives characterized by distortion riskmetrics.

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