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arXiv Machine Learning By Zihao Shi, Huajun Xi, Bingyi Jing, Hongxin Wei

Occupancy-based Quantile Risk Control

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Occupancy-based Quantile Risk Control (OQRC) is a new method that extends conformal risk control to quantile-based risk measures. It partitions the loss space using ordered calibration losses, estimates the distribution of test losses in each bin, and upper-bounds the risk by the maximum loss per bin. The approach guarantees finite-sample validity, achieving tight risk control bounds that converge at a rate of σ(n^{-1/2}) and reducing the risk gap by up to 78.64% in experiments.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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Occupancy-based Quantile Risk Control (OQRC) is a new method that extends conformal risk control to quantile-based risk measures while avoiding excessive conservatism and providing rigorous finite-sample guarantees. It works by partitioning the loss space using ordered calibration losses, estimating the distribution of test losses in each bin, and bounding the risk by the maximum loss in each bin. The authors prove that OQRC achieves tight risk control bounds with a finite-sample guarantee that converges at a rate of π(n−½), and experiments show it can reduce the risk gap by up to 78.64% on common benchmarks.

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