Hugging Face Trending Papers

Occupancy-based Quantile Risk Control

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.

arXiv Machine Learning

Sep 4

Occupancy-based Quantile Risk Control

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.

By Zihao Shi, Huajun Xi, Bingyi Jing, Hongxin Wei

benchmarks

arXiv Machine Learning

Sep 14

Quantile-based Loss Filtering for Outlier-Robust Stochastic Gradient Descent

arXiv:2609. 13040v1 Announce Type: new Abstract: We study loss-based filtering for finite-sum optimization with a subset of corrupted component functions whose gradients may be highly unreliable.

By Jamie Haddock, Anna Ma, Elizaveta Rebrova

arXiv Machine Learning

Jun 11

Calibrating Decision Robustness via Inverse Conformal Risk Control

arXiv:2510. 07750v3 Announce Type: replace-cross Abstract: Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions.

By Wenbin Zhou, Shixiang Zhu

arXiv Machine Learning

Sep 22

Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift

arXiv:2609.24929v1 Announce Type: cross Abstract: In this paper, we study nonasymptotic $L^p$ error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR...

By Rustam Isaev, Anton Conrad, Denis Belomestny, Eric Moulines, Sergey Samsonov

benchmarks

arXiv Machine Learning

Sep 1

A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification

arXiv:2608.29789v1 Announce Type: cross Abstract: Uncertainty quantification from finite data is central to machine learning, optimization, and automation systems, where decisions must remain reliabl...

By Kehan Long, Yiqi Zhao, Pol Mestres, Lars Lindemann, Nikolay Atanasov, Jorge Cort\'es

arXiv Machine Learning

Jul 30

Simultaneous Coverage and Efficiency Guarantee in Online Conformal Prediction

arXiv:2607. 26577v1 Announce Type: new Abstract: Adaptive conformal inference (ACI) of Gibbs and Cand{\`e}s and its variants are the standard approach to online conformal prediction under distribution shift, but they suffer from three fundamental limitations.

By Rahul Vaze

benchmarks safety

arXiv Machine Learning

Jun 5

Conformal Risk-Averse Decision Making with Action Conditional Guarantee

arXiv:2606. 05551v1 Announce Type: cross Abstract: Reliable decision making pipelines powered by machine learning models require uncertainty quantification (UQ) methods that come with explicit safety guarantees.

By Zihan Zhu, Shayan Kiyani, George Pappas. Hamed Hassani

safety

arXiv Machine Learning

Sep 2

Online simultaneous inference for quantiles via smoothed stochastic gradient descent

arXiv:2505.13299v2 Announce Type: replace-cross Abstract: This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the...

By Likai Chen, Georg Keilbar, Wei Biao Wu

efficiency

arXiv Machine Learning

Sep 10

Understanding Uncertainty Sampling via Equivalent Loss

arXiv:2307.02719v5 Announce Type: replace Abstract: Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introdu...

By Shang Liu, Xiaocheng Li

arXiv Machine Learning

Sep 11

Certifying Lower Bounds for Risk-Sensitive Reinforcement Learning under Adversarial State Perturbations

arXiv:2609. 10866v1 Announce Type: new Abstract: Reinforcement learning (RL) agents deployed in real-world environments are often vulnerable to adversarial perturbations in state observations, creating risks in safety-critical applications.

By Tong Li, Saunak Kumar Panda, Yisha Xiang

agents reinforcement-learning safety

arXiv Machine Learning

Jun 9

A Joint Finite-Sample Certificate for Adaptive Selective Conformal Risk Control

arXiv:2606. 08517v1 Announce Type: new Abstract: Selective predictors answer on confident inputs and abstain elsewhere; deploying one safely needs a single finite-sample certificate that simultaneously upper-bounds the selected risk, lower-bounds the acceptance probability $\pacc$ above a floor $\pmin$, and lower-bounds the deployment utility.

By Xiaoli Yu, Jiamiao Liu

arXiv Machine Learning

Jun 8

Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite $L_p$ Moments

arXiv:2606. 06855v1 Announce Type: cross Abstract: While algorithmic stability is a central tool for understanding generalization of learning algorithms, existing high-probability guarantees typically rely on uniform boundedness or sub-Gaussian/sub-Weibull tail assumptions, which can be overly restrictive for modern settings with heavy-tailed or unbounded losses.

By Qianqian Lei, Soham Bonnerjee, Yuefeng Han, Wei Biao Wu

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