Uncertainty Calibration

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 184

All topics

Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift
May 6, 2026Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu +4Distribution Shift 3D Object Detection
Institute for Automotive Engineering, RWTH Aachen · Computer Vision Institute, RWTH Aachen
LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy
May 5, 2026Hamed Karimi, Vaishali Meyappan, Reza SamaviSelective Prediction Semantic Entropy
Toronto Metropolitan University, Toronto, Ontario, Canada · Vector Institute, Toronto, Ontario, Canada
Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction
May 4, 2026Jonas V. FunkUncertainty Quantification Wildfire Forecasting
Independent Research 76199 Karlsruhe, Germany
Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics
May 3, 2026Fernando Martin-Maroto, Nabil Abderrahaman, Gonzalo G. de PolaviejaProbability Calibration Uncertainty Calibration
Mathematics of Behavior and Intelligence Lab. Champalimaud Research. Avenida de Brasília, Lisbon · Algebraic AI Calle Castelló, 117, Madrid
Hallucinations Undermine Trust; Metacognition is a Way Forward
May 2, 2026Gal Yona, Mor Geva, Yossi MatiasLLM Hallucination Mitigation Hallucination in Language Models
Google Research · Tel Aviv University
Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes
Apr 30, 2026Eichi UeharaRobust Regression Causal Effect Estimation
Independent researcher
Stable but Wrong: An Inference Limit in Galactic Archaeology
Apr 30, 2026Zhipeng ZhangUncertainty Calibration
China Mobile Research Institute, Beijing 100053, China · China Mobile GBA (Greater Bay Area) Innovation Institute, Guangzhou 510656, China
Recipes for Calibration Checks in Safety-Critical Applications
Apr 29, 2026Romeo ValentinProbabilistic Forecasting Uncertainty Calibration
Department of Aeronautics and Astronautics Stanford University Stanford, CA 94305, USA
Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models
Apr 22, 2026Shelly Francis-Meretzki, Mirco Mutti, Yaniv Romano +1Uncertainty Estimation for VLA Models Vision-Language-Action Models
1Technion - Israel Institute of Technology.
Calibrating Scientific Foundation Models with Inference-Time Stochastic Attention
Apr 21, 2026Akash Yadav, Taiwo A. Adebiyi, Ruda ZhangPost-Hoc Calibration Probabilistic Forecasting
University of Houston
Answer Only as Precisely as Justified: Calibrated Claim-Level Specificity Control for Agentic Systems
Apr 19, 2026Tianyi Huang, Samuel Xu, Jason Tansong Dang +2Uncertainty Calibration LLM Agent Reliability
Ryquo · App-In Club.
Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
Apr 18, 2026Khemraj Shukla, Zongren Zou, Theo Kaeufer +2Uncertainty Quantification Deep Ensembles
Division of Applied Mathematics, Brown University, Providence, RI, USA · Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA · Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Complementing Self-Consistency with Cross-Model Disagreement for Uncertainty Quantification
Apr 18, 2026Kimia Hamidieh, Veronika Thost, Walter Gerych +2Selective Prediction Confidence Estimation in Language Models
1MIT · MIT-IBM Watson AI Lab · 3WPI +1
QuantSightBench: Evaluating LLM Quantitative Forecasting with Prediction Intervals
Apr 17, 2026Jeremy Qin, Maksym AndriushchenkoLLM Evaluation Forecasting Benchmarks
ELLIS Institute Tübingen · Max Planck Institute for Intelligent Systems · Tübingen AI Center
Convolutionally Low-Rank Models with Modified Quantile Regression for Interval Time Series Forecasting
Apr 17, 2026Miaoxuan Zhu, Yi Yu, Yuyang Li +2Quantile Regression Time Series Forecasting
Southeast University
SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation
Apr 16, 2026Tianhao Fu, Austin Wang, Charles Chen +2Image Segmentation Uncertainty Quantification
University of Toronto, Toronto, ON, Canada · Vector Institute, Toronto, ON, Canada · Project Neura, Toronto, ON, Canada +4
CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction
Apr 16, 2026Sizhe Wang, Ziqi Xu, Claire Najjuuko +2Language Model Calibration LLM Uncertainty Estimation
Washington University in St. Louis
Measuring (some aspects of) the metacognition of AI
Mar 31, 2026Richard Servajean, Philippe ServajeanLLM Evaluation LLM Uncertainty Estimation
Center for Brain Science, RIKEN, Saitama, Japan. · National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. · Department of Psychology, Paul-Val´ery University, Montpellier, France.
Rethinking Uncertainty Quantification and Entanglement in Image Segmentation
Mar 19, 2026Jakob Lønborg Christensen, Vedrana Andersen Dahl, Morten Rieger Hannemose +2Uncertainty Quantification Deep Ensembles
Technical University of Denmark, DTU Compute · University of Lucerne, Faculty of Health Sciences and Medicine, Switzerland
Trustworthy Predictive Distributions for Tail Events with Semiparametric Diagnostic Transport Maps
Mar 11, 2026Elizabeth Cucuzzella, Rafael Izbicki, Ann B. LeeProbability Calibration Probabilistic Forecasting
Department of Statistics and Data Science, Carnegie Mellon University · Department of Statistics, Federal University of Sao Carlos
Rethinking Gaussian Trajectory Predictors: Calibrated Uncertainty for Safe Planning
Mar 11, 2026Fatemeh Cheraghi Pouria, Mahsa Golchoubian, Katherine Driggs-CampbellPedestrian Trajectory Prediction Safe Motion Planning
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820 USA · Department of Mathematical and Computational Sciences, University of Toronto, Toronto, Canada
V-ECE: Estimating General Expected Calibration Errors
Feb 27, 2026Eugène Berta, Sacha Braun, Francis Bach +2Probability Calibration Uncertainty Calibration
INRIA · Ecole Normale Sup´erieure, PSL Research University · UC Berkeley
Confidence-Uncertainty Boundary Calibration for Bayesian Deep Learning in Medical Image Analysis
Feb 12, 2026Hua Xu, Julián D. Arias-Londoño, Juan I. Godino-LlorenteBayesian Neural Networks Post-Hoc Calibration
Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid 28040, Madrid, Spain
Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning
Feb 1, 2026Pietro Carlotti, Nevena Gligić, Arya FarahiEvidential Deep Learning OOD Detection
Department of Statistics and Data Sciences University of Texas at Austin
When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning
Jan 12, 2026Chenjie Hao, Weyl Lu, Yuko Ishiwaka +3Adaptive Inference Confidence Estimation in Language Models
University of California, Davis · SoftBank Corp. · Aizip
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
Jan 7, 2026Ibai Ramirez, Jokin Alcibar, Joel Pino +2Bayesian Neural Networks Uncertainty Quantification
Department of Electronics and Computer Science Mondragon University Arrasate-Mondragon, 20500, Spain · CARTIF Technology Centre Valladolid, 47151, Spain · Department of Physical Chemistry University of the Basque Country (EHU)[cs.LG] Basque Centre for Applied Mathematics (BCAM) Leioa, 20018, Spain +1
Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes
Dec 16, 2025Joseph Hoche, Andrei Bursuc, David Brellmann +4Vision-Language Models Uncertainty Quantification
AMIAD, Pôle Recherche, Palaiseau · Safran Tech · University of Liège +3
MedBayes-Lite: A Clinical Uncertainty Governance Layer for Risk-Aware Medical Decision Support
Nov 20, 2025Elias Hossain, Md Mehedi Hasan Nipu, Maleeha Sheikh +5Selective Prediction Healthcare
College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA · Department of Computer Science and Engineering, North South University, Dhaka 1229, Bangladesh · Department of Electrical and Computer Engineering, Purdue University Fort Wayne, Fort Wayne, IN 46805, USA +4
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Jul 21, 2025Gábor Lugosi, Marcos MatabuenaConformal Prediction k-Nearest Neighbors
Department of Economics and Business, Pompeu Fabra University, Barcelona, Spain · ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain · Barcelona Graduate School of Economics +3
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Jul 10, 2025Ilia Azizi, Juraj Bodik, Jakob Heiss +1Uncertainty Quantification Aleatoric Uncertainty
Department of Operations, HEC, University of Lausanne, Switzerland · BegooAI, Switzerland · Department of Statistics, University of California, Berkeley, USA +1

May 6, 2026, cs.CVJ/K move · Enter open · S save

Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift

Till Beemelmanns, Alexey Nekrasov, Stefan Vilceanu, Jonas Steinhaus+3

Institute for Automotive Engineering, RWTH Aachen · Computer Vision Institute, RWTH Aachen

Distribution Shift 3D Object Detection Density Estimation Uncertainty Calibration

Open paper AskPDF arXiv

Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribution shifts. Although post-hoc calibration methods address this issue and provide improved calibration for in-distribution tests, they fail to adapt in distribution-shifted scenarios. In this work, we address this issue and introduce a density-aware calibration method that couples post-hoc calibrators with the feature density of latent object queries from DETR-style 3D object detectors. These queries form a compact, location and class-aware feature, ideal for density estimation, allowing our approach to adjust model confidences in distribution-shift scenarios. By fitting a density estimator on these query features, our approach jointly recalibrates both classification and bounding box regression uncertainties. On both a multi-view camera and LiDAR-based detector, our approach consistently outperforms standard post-hoc methods in both in-distribution and distribution-shifted scenarios. Code available https://tillbeemelmanns.github.io/query2uncertainty/ .

Momentum

Related topics

Latest papers 184