Uncertainty Calibration

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15 papers in the last four weeks, up 7% on the four weeks before. 0.1% of all new papers.

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Online Conformal Prediction with Corrupted Feedback
May 19, 2026Bowen Wang, Matteo Zecchin, Osvaldo SimeoneConformal Prediction Uncertainty Calibration
Department of Engineering, King’s College London, WC2R 2LS London, U.K. · Communication Systems Department, EURECOM, 06410 Sophia Antipolis, France · Institute for Intelligent Networked Systems, Northeastern University London, E1W 1LP London, U.K.
CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support
May 19, 2026Ricardo Diaz-Rincon, Muxuan Liang, Adolfo Ramirez-Zamora +1Clinical Decision Support Conformal Prediction
University of Florida · MD Anderson Cancer Center · University of Louisville
Reading Calibrated Uncertainty from Language Model Trajectories
May 19, 2026Aliai Eusebi, Alexander Herzog, Xiaoyu Liang +3Confidence Estimation in Language Models LLM Reliability
University College London, London, United Kingdom · Core46, London, United Kingdom
Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization
May 19, 2026Aurélien Pion, Emmanuel VazquezPost-Hoc Calibration Bayesian Optimization
Transvalor S.A., Biot, France · Univ. Paris-Saclay, CNRS, CentraleSup´elec, L2S, Gif-sur-Yvette, France
An Assessment of Human vs. Model Uncertainty in Soft-Label Learning and Calibration
May 18, 2026Maja Pavlovic, Silviu Paun, Massimo PoesioSoft-Label Learning Human-in-the-Loop Annotation
Queen Mary University London · Amazon · Queen Mary University London - University of Utrecht
Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation
May 18, 2026Tristan Kirscher, Markus Bujotzek, Yannick Kirchhoff +5Deep Ensembles Medical Image Segmentation
ICube, Institut Strauss, DKFZ · ICube Laboratory, CNRS UMR-7357, University of Strasbourg, Strasbourg, France · CLCC Institut-Strauss, Strasbourg, France +6
Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography
May 18, 2026Mohammad Moulaeifard, Ciaran Bench, Philip J. Aston +1Distribution Shift Robustness Post-Hoc Calibration
AI4Health Department, University of Oldenburg, Oldenburg, Germany · Department of Data Science and AI, National Physical Laboratory, Teddington, United Kingdom · School of Mathematics and Physics, University of Surrey, Surrey, United Kingdom
Uncertainty-Calibrated Recommendations for Low-Active Users
May 18, 2026Bob Junyi Zou, Sai Li, Tianyun Sun +2Uncertainty Quantification Exploration-Exploitation Tradeoff
Stanford University · Stanford, CA, USA · ByteDance Inc. +2
Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment
May 15, 2026Till Beemelmanns, Shayan Sharifi, Manas Mehrotra +2Explainable Artificial Intelligence Autonomous Driving Perception
Institute for Automotive Engineering, RWTH Aachen University, 52074 Aachen, Germany
Uncertainty-Aware Wildfire Smoke Density Classification from Satellite Imagery via CBAM-Augmented EfficientNet with Evidential Deep Learning
May 15, 2026Ranjith ChodavarapuRemote Sensing Image Understanding Evidential Deep Learning
Kent State University
U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable exploration
May 14, 2026Michael Smith, Frank P. FerrieImage Segmentation Panoptic Segmentation
Centre for Intelligent Machines, McGill University
Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems
May 14, 2026Yuxin Guo, Dongrui Deng, Pulkit GroverUncertainty Quantification Bayesian Inverse Problems
1Neuroscience Institute, Carnegie Mellon University · 2Machine Learning Department, Carnegie Mellon University · Department of Electrical and Computer Engineering, Carnegie Mellon University
Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks
May 13, 2026Lingfei Kong, Haoran MaMedical Image Analysis Longitudinal Medical Image Analysis
Department of Mathematics, Vanderbilt University, Nashville, TN, USA · John A. Paulson School of Engineering and Applied Science, Harvard University, Allston, MA, USA
Discovery of Hidden Miscalibration Regimes
May 13, 2026Katarzyna Kobalczyk, Mihaela van der SchaarPost-Hoc Calibration Language Model Calibration
University of Cambridge
Online Conformal Prediction: Enforcing monotonicity via Online Optimization
May 12, 2026Eduardo Ochoa Rivera, Ambuj TewariConformal Prediction Uncertainty Calibration
University of Michigan · Ann Arbor, USA
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
May 12, 2026Julian Rodemann, Alexander Marquard, Thomas Augustin +1Bayesian Neural Networks Uncertainty Quantification
Rational Intelligence Lab, CISPA Helmholtz Center for Information Security · Department of Statistics, LMU Munich · Department of Computer Science, The University of Manchester
Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty
May 12, 2026Haoran Hu, Xingce WangParameter Estimation Latent Variable Models
School of Artificial Intelligence Beijing Normal University Beijing 100875, China
Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification
May 12, 2026Mehmet Ali Ferah, Tufan KumbasarUncertainty Quantification Nonlinear System Identification
Artificial Intelligence and Intelligent Systems Laboratory, Istanbul Technical University, Istanbul, Türkiye
Online Sharp-Calibrated Bayesian Optimization
May 11, 2026Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen +1Bayesian Optimization Uncertainty Calibration
ELLIS Institute Finland · Aalto University · University of Manchester
Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis
May 11, 2026Junyu Lu, Deyi Ji, Xuanyi Liu +5Annotator Disagreement LLM Alignment
School of Computer Science and Technology, Dalian University of Technology, China · Tencent · Peking University +1
Unified Approach for Weakly Supervised Multicalibration
May 11, 2026Futoshi Futami, Takashi IshidaPost-Hoc Calibration Model Calibration
The University of Osaka / RIKEN AIP / The University of Tokyo · The University of Tokyo / RIKEN AIP
Free Energy Manifold: Score-Based Inference for Hybrid Bayesian Networks
May 11, 2026Cheol Young Park, Shou MatsumotoBayesian Networks Energy-Based Models
ATOS Co., Ltd., 273, Digital-ro, Guro-gu, Seoul, 08381, Republic of Korea · C5I Center, George Mason University, Fairfax, VA 22030, USA
Medical Model Synthesis Architectures: A Case Study
May 10, 2026Katherine M. Collins, Marlene Berke, Ilia Sucholutsky +6Explainable Artificial Intelligence Healthcare
MIT · University of Cambridge · Princeton University +8
The Minimax Rate of Perturbed Second-Order Calibration
May 8, 2026Kamil Ciosek, Banafsheh Rafiee, Sina Ghiassian +1Post-Hoc Calibration Minimax Estimation
Spotify
Probabilistic Object Detection with Conformal Prediction
May 8, 2026Christopher Ries, Moussa Kassem Sbeyti, Nicolas Bianco +1Autonomous Driving Perception Conformal Prediction
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport
May 7, 2026Rachel Ma, Dylan Hadfield-Menell, Kristjan GreenewaldProcess Reward Models Inference-Time Scaling
MIT CSAIL · MIT-IBM Watson AI Lab · IBM Research
Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
May 7, 2026Steven Wilkins-Reeves, Alexandra N. M. Darmon, Deeksha SinhaDistribution Shift Robustness Uncertainty Calibration
Central Applied Science, Meta · New York, NY, USA · Menlo Park, CA, USA
Probabilistic Dating of Historical Manuscripts via Evidential Deep Regression on Visual Script Features
May 7, 2026Ranjith ChodavarapuHistorical Document Analysis Evidential Deep Learning
Kent State University
CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision
May 7, 2026Liang Qian, Penggao Yan, Penghui Xu +1Factor Graph Optimization Wireless Localization
Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University, Hong Kong, China
Uncertainty Estimation via Hyperspherical Confidence Mapping
May 7, 2026Eunseo Choi, Ho-Yeon Kim, Jaewon Lee +3Uncertainty Quantification Uncertainty Calibration
KAIST · Samsung Electronic Co., Ltd

May 19, 2026, cs.LGJ/K move · Enter open · S save

Online Conformal Prediction with Corrupted Feedback

Bowen Wang, Matteo Zecchin, Osvaldo Simeone

Department of Engineering, King’s College London, WC2R 2LS London, U.K. · Communication Systems Department, EURECOM, 06410 Sophia Antipolis, France · Institute for Intelligent Networked Systems, Northeastern University London, E1W 1LP London, U.K.

Conformal Prediction Uncertainty Calibration

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Modern artificial intelligence systems require calibrated uncertainty estimates that remain reliable in sequential and non-stationary environments. Online conformal prediction (OCP) addresses this challenge through adaptively updated prediction sets that provide deterministic long-run miscoverage guarantees. These guarantees, however, hinge on the assumption of perfect feedback about the coverage of past prediction sets. In practice, the observed miscoverage indicator may be corrupted by noise, communication failures, or adversarial manipulation, which can severely degrade OCP's calibration guarantees. In this paper, we study OCP under corrupted feedback. We first model feedback corruption as an arbitrary binary flip sequence, and analyze how feedback corruption affects and degrades the miscoverage performance of standard OCP. We then propose two robust schemes: robust OCP via filtering, which leverages the structural properties of the predicted threshold to filter corrupted feedback, and robust OCP via active compensation, which incorporates an active compensation mechanism to mitigate the effect of corrupted feedback. For both methods, we establish explicit miscoverage guarantees, which are further specialized for an independent stochastic flip model and for an arbitrary error model with memory bounds. Experiments on real-world datasets validate the proposed approach, showing markedly improved calibration and significantly smaller prediction sets compared with baseline OCP methods under corrupted feedback.

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Latest papers 184