Occupancy-based Quantile Risk Control

2026-09-02

AI Summary

为解决现有方法在分位数风险控制中的保守性和缺乏有限样本保证问题，提出基于占用的分位数风险控制（OQRC）方法，通过将损失空间分区并估计测试损失分布来提供紧致的风险控制界限。

Abstract

Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite-sample guarantees. To accommodate a broader class of risk notions, quantile risk control extends this framework to quantile-based risk measures. However, existing methods either suffer from excessive conservatism or lack rigorous finite-sample guarantees. To address these limitations, we introduce Occupancy-based Quantile Risk Control (OQRC), a novel method that provides tight risk control bounds with finite-sample validity. Our key idea is to formulate risk control as a finite-occupancy problem by partitioning the loss space with the ordered calibration losses. Specifically, we estimate the distribution of test losses across the resulting bins and upper-bound the risk by the maximum loss attained within each bin. We then select the parameter $λ$ such that this upper bound does not exceed a predefined threshold $α$ with high probability $1-δ$. Theoretically, we establish a finite-sample guarantee showing that OQRC yields tight risk control bounds that converge to the optimal bounds at a provable rate of $\mathcal{O}_ p(n^{-1/2})$. Extensive experiments demonstrate the effectiveness of our method, reducing the risk gap by up to 78.64\% on common benchmarks.

Problem

• Quantile Risk Control
• Finite-sample Guarantees
• Conformal Risk Control

Innovation

• Occupancy-based Quantile Risk Control
• finite-sample guarantees
• loss space partitioning
• risk control bounds