Occupancy-based Quantile Risk Control

• URL: http://arxiv.org/abs/2609.03104v1
• Date: Wed, 02 Sep 2026 19:32:46 GMT
• Title: Occupancy-based Quantile Risk Control
• Abstract summary: We introduce Occupancy-based Quantile Risk Control (OQRC), a novel method that provides tight risk control bounds with finite-sample validity.<n>Our key idea is to formulate risk control as a finite-occupancy problem by partitioning the loss space with the ordered calibration losses.<n>Experiments demonstrate the effectiveness of our method, reducing the risk gap by up to 78.64% on common benchmarks.
• Score: 21.91768834819608
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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