Confidence Region Estimation

Follow this topic Ask about this topic

Confidence region estimation studies how to construct sets that contain an unknown parameter or future outcome with a specified probability, making decisions more reliable than point estimates alone. Approaches include conformal prediction and other distribution-free methods, bootstrap and asymptotic approximations, and self-normalized or robust procedures for dependent, private, or heavy-tailed data. Methods are increasingly tailored to settings such as reinforcement learning, selective classification, and inference with predicted or missing labels, where data collection and dependence affect coverage. A central tension is obtaining valid coverage under weak assumptions while keeping regions small and computation practical.

Latest papers

39 papers5 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

Online Statistical Inference of Constant Sample-averaged Q-Learning
Mar 27, 2026Saunak Kumar Panda, Tong Li, Ruiqi Liu +1Reinforcement Learning Q-Learning
Department of Industrial Engineering, University of Houston · Department of Mathematics and Statistics, Texas Tech University
Kernel Single-Index Bandits: Estimation, Inference, and Learning
Mar 19, 2026Sakshi Arya, Satarupa Bhattacharjee, Bharath K. SriperumbudurContextual Bandits Single-Index Models
Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University · Department of Statistics, University of Florida · Department of Statistics, The Pennsylvania State University
Spatially Robust Inference with Predicted and Missing at Random Labels
Mar 11, 2026Stephen Salerno, Zhenke Wu, Tyler McCormickConfidence Region Estimation Learning with Missing Data
Fred Hutch Cancer Center · University of Michigan · University of Washington
Prediction-Powered Conditional Inference
Mar 5, 2026Yang Sui, Jin Zhou, Hua Zhou +1Prediction-Powered Inference Confidence Region Estimation
University of California, Los Angeles
Noise-Calibrated Inference from Differentially Private Sufficient Statistics in Exponential Families
Mar 2, 2026Amir Asiaee, Samhita PalExponential Family Differential Privacy
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
Estimation of Confidence Bounds in Binary Classification using Wilson Score Kernel Density Estimation
Feb 24, 2026Thorbjørn Mosekjær Iversen, Zebin Duan, Frederik HagelskjærBinary Classification Confidence Region Estimation
SDU Robotics, The Maersk Mc-Kinney Moller Institute, University of Southern
Approximate full conformal prediction in an RKHS
Jan 19, 2026Davidson Lova Razafindrakoto, Alain Celisse, Jérôme LacailleConfidence Region Estimation Conformal Prediction
Laboratoire SAMM Université Paris 1 Panthéon-Sorbonne 90, rue de Tolbiac 75634 PARIS CEDEX 13, FRANCE · Safran Aircraft Engines Rond-Point René Ravaud, Réau, 77550 Moissy-Cramayel CEDEX, France
Statistical Inference for Policy Evaluation with Temporal Difference Learning
Oct 21, 2024Weichen Wu, Gen Li, Yuting Wei +1Parameter Estimation Confidence Region Estimation
The Voleon Group, New York, NY, USA · Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR. · Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. +1
Distribution-Free Uncertainty Quantification for Kernel Methods by Gradient Perturbations
Dec 23, 2018Balázs Csanád Csáji, Krisztián Balázs KisConfidence Region Estimation Uncertainty Quantification
EPIC Centre of Excellence MTA SZTAKI: Institute for Computer Science and Control Hungarian Academy of Sciences, Budapest, Hungary

Mar 27, 2026J/K move · Enter open · S save

Online Statistical Inference of Constant Sample-averaged Q-Learning

Saunak Kumar Panda, Tong Li, Ruiqi Liu, Yisha Xiang

Department of Industrial Engineering, University of Houston · Department of Mathematics and Statistics, Texas Tech University

Reinforcement Learning Q-Learning Uncertainty Estimation in RL Confidence Region Estimation

Open paper AskPDF arXiv

Reinforcement learning algorithms have been widely used for decision-making tasks in various domains. However, the performance of these algorithms can be impacted by high variance and instability, particularly in environments with noise or sparse rewards. In this paper, we propose a framework to perform statistical online inference for a sample-averaged Q-learning approach. We adapt the functional central limit theorem (FCLT) for the modified algorithm under some general conditions and then construct confidence intervals for the Q-values via random scaling. We conduct experiments to perform inference on both the modified approach and its traditional counterpart, Q-learning using random scaling and report their coverage rates and confidence interval widths on two problems: a grid world problem as a simple toy example and a dynamic resource-matching problem as a real-world example for comparison between the two solution approaches.