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

By Yang Liu, Yuhao Liu, Yunran Wei

Published 2026-07-15

Everscope rating

1511.9

Relevance to quantitative trading

8 / 10

Implementation complexity

7 / 10

Reproducibility

3 / 5

About this paper

Methodology: Noise-Robust Elicit-to-Optimize Framework (Bayesian IRL + Extended PPO with Quantile Networks). Problem types: Reinforcement Learning, Risk Management, Portfolio Optimization, Optimization, Active Learning, Online Learning.

arXiv:2607.14373 · Paper rankings

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