Uncertainty Quantification

Also known as UQ

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44 papers in the last four weeks, up 110% on the four weeks before. 0.4% of all new papers.

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SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
Sep 23, 2026Xin-Yu Hu, Shuang Liang, Cheng Feng +1Uncertainty Quantification Time Series Forecasting
National Key Laboratory for Novel Software Technology, Nanjing University, China. · School of Intelligent Science and Technology, Nanjing University, China. · Siemens Data and AI Research, Beijing, China. +1
KITE: Scaling Jev Population Experiments with Sparse Flagship Calibration
Sep 23, 2026Hengyu LiCausal Effect Estimation Uncertainty Quantification
The University of Tokyo
Predictive Uncertainty for Neural CAE Surrogates
Sep 21, 2026Kaustubh Tangsali, Mohammad Amin Nabian, Kelvin Lee +2Neural Surrogate Modeling Uncertainty Quantification
NVIDIA
MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting
Sep 21, 2026Tsung Yeh Hsieh, Cosmin Anitescu, Chunghwan Kim +2Surrogate Modeling Uncertainty Quantification
Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA · Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA · Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers
Sep 17, 2026Jiachen Yao, Zi-Siang Hsu, Xi Deng +5Uncertainty Quantification Bayesian Inverse Problems
California Institute of Technology · National Taiwan University · Lawrence Berkeley National Laboratory +3
Compressed Active Subspaces for Scalable Bayesian Inference
Sep 17, 2026Thomas Flynn, Sanket Jantre, Byung-Jun Yoon +1Uncertainty Quantification Bayesian Inference
Computing and Data Sciences Directorate, Brookhaven National Laboratory, Upton, NY, USA · Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA · Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
Predicting Human Disagreement for Calibrated Dynamic Facial Expression Recognition
Sep 15, 2026Yiming Wang, Frederick W. B. Li, Jingyun WangAnnotator Disagreement Uncertainty Quantification
Department of Computer Science, Durham University
Nationally Consistent, Locally Incomplete: A Bayesian Remote-Sensing Audit of Rooftop Photovoltaic Registries
Sep 14, 2026Gabriel Kasmi, Yves-Marie Saint-Drenan, Laurent Dubus +1Uncertainty Quantification Remote Sensing
Centre Observation Impacts Energie (O.I.E.), MINES Paris, Universit´e PSL, Sophia-Antipolis, France · R´eseau de Transport d’Electricit´e (RTE), Paris La D´efense, France · Lead contact +1
SURE-Map: Self-Correcting Streaming Geometric Foundation Models
Sep 14, 2026Mingkai Liu, Hao Zhao, Xingxing ZuoStreaming 3D Reconstruction Uncertainty Quantification
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), UAE. · Peking University, China. · Tsinghua University, China.
Draining Fictitious Knots: Restoring Distance-Awareness Guarantees for High-Dimensional Spline Networks
Sep 14, 2026Masoud Ataei, Mohammad Javad Khojasteh, Vikas DhimanUncertainty Quantification Kolmogorov-Arnold Networks
Electrical and Computer Engg. University of Maine Orono, ME, USA · Electrical and Microelectronic Engg. Rochester Institute of Technology Rochester, NY, USA
Closed-form Bayesian homography estimation from noisy point correspondences
Sep 14, 2026Hanne Beuter, Sebastian DornMulti-View Geometry Uncertainty Quantification
Technische Hochschule Augsburg · TTZ Landsberg am Lech · Augsburg, Germany
Rethinking Correctness for Uncertainty Estimation in Clinical Prediction with Vision-Language Models
Sep 14, 2026Mingcheng Zhu, Jinning Liang, Tingting ZhuUncertainty Quantification Medical VLMs
University of Oxford, Oxford, United Kingdom
Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators
Sep 14, 2026Michael ChinFourier Neural Operator Uncertainty Quantification
Independent Researcher
Uncertainty-Aware Conflict Detection Against Operator-Conditioned Weather Hazards
Sep 14, 2026Balram Kandoria, Seulki Kim, Aryaman Singh SamyalUncertainty Quantification Air Traffic Management
SkyGrid, Austin, TX 78727, USA
4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling
Sep 14, 2026Deifilia Kieckhefen, Juan Pedro Gutiérrez Hermosillo Muriedas, Lars Helge Heyen +12Bayesian Neural Networks Uncertainty Quantification
Karlsruhe Institute of Technology, Germany · Forschungszentrum Jülich, Germany · CSC – IT Center for Science Ltd., Finnland +1
Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models
Sep 13, 2026Ji Lu, Huiran Duan, Bo Zhao +5Uncertainty Quantification Geospatial Foundation Models
Vanderbilt University, United States · City University of New York, United States · Yale University, United States +4
Estimating Inconsistency Response Surfaces under Uncertainty in Cyber-Physical System Development
Sep 11, 2026Johannes Mäkelburg, Tim Schwabe, Maribel AcostaCyber-Physical Systems Surrogate Modeling
Technische Universität München Munich, Germany
Distilling Image Prototypes for Guided Test-Time Adaptation
Sep 9, 2026Liwen Wang, Xingbo Dong, Iman Yi Liao +4Source-Free Domain Adaptation Test-Time Adaptation
Anhui Provincial Key Laboratory of Secure Artificial Intelligence, Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Artificial Intelligence, Anhui University, Hefei 230093, China · School of Computer Science, University of Nottingham (Malaysia Campus), Selangor Darul Ehsan, 43500, Malaysia · Department of Engineering, University of Sassari, Sassari, Italy +1
Uncertainty-Aware Sea-Ice Type Mapping with Multiple Ice Charts
Sep 8, 2026Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1Remote Sensing Image Understanding Annotator Disagreement
University of Colorado Denver Denver, Colorado, USA · National Snow and Ice Data Center (NSIDC), CIRES, University of Colorado Boulder Boulder, Colorado, USA
Accountable and uncertainty-aware evaluation of sensor-based AI under distribution shift: devices, subjects, and nearly three years underground
Sep 8, 2026Benny Platte, Rico Thomanek, Christian Roschke +1AI Accountability Uncertainty Quantification
Mittweida University of Applied Sciences · Faculty of Applied Computer Sciences and Biosciences, Mittweida University of Applied Sciences, Mittweida, Germany
Flexible Spectral-Normalized Neural Gaussian Process for Dynamic Aperture Prediction
Sep 8, 2026Yousra El-Bachir, Frederik Van der Veken, Davide di Croce +4Neural Surrogate Modeling Uncertainty Quantification
Swiss Data Science Center, ETH Zurich & EPFL · CERN, Geneva · EPFL, Lausanne; CERN, Geneva
Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models
Sep 8, 2026Simon Adamov, Oliver Fuhrer, Reto Knutti +1Uncertainty Quantification Ensemble Learning
Federal Office for Meteorology and Climatology MeteoSwiss, Zurich, Switzerland · Institute for Atmospheric and Climate Science (IAC), ETH Zurich, Zurich, Switzerland · Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom
Geometry-Aware Bayesian Parameter-Efficient Fine-Tuning on the Stiefel Manifold via Stein Variational Gradient Descent
Sep 8, 2026Quang-Duy Tran, Trung Le, Bao Duong +2Stiefel Manifold Optimization Uncertainty Quantification
Deakin Applied Artificial Intelligence Initiative, Deakin University, Geelong, Australia · Department of Computer Science, Aalto University, Espoo, Finland · Department of Data Science & AI, Monash University, Melbourne, Australia
The Role of Uncertainty in Assessing the Fairness of Machine Learning Models
Sep 7, 2026Francesca Panero, Ernst C. Wit, Marco ScutariUncertainty Quantification Algorithmic Fairness
Department of Methods and Models for Economics, Territory and Finance, Sapienza Universit`a di Roma, Rome, 00161, Italy · Department of Statistics, London School of Economics and Political Science, London, WC2B 4RR, UK · Faculty of Informatics, Universit`a della Svizzera Italiana (USI), Lugano, 6962, Switzerland +1
FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection
Sep 2, 2026Zhaoyang Wang, Haiyong Chen, Binyi Su +1Active Learning Uncertainty Quantification
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
Verdict Instability of OOD Scores under Reference Resampling
Sep 1, 2026Donghoon Lee, Shinjin KangUncertainty Quantification Selective Prediction
Hongik University
AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials
Aug 31, 2026Prajwal Ananth, Shuwen YueUncertainty Quantification Machine Learning Interatomic Potentials
Center for Applied Mathematics, Cornell University, Ithaca, NY, USA · R. F. Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA
Reliable Benchmarking of Artifact Detection in Computational Pathology: A Reproducibility and Uncertainty Analysis
Aug 31, 2026Konstantinos Moutselos, Ilias MaglogiannisComputational Pathology Uncertainty Quantification
Department of Digital Systems, University of Piraeus, Piraeus, Greece
Aggregate Disambiguation Systems
Aug 31, 2026José María Lago, Albert Castellana, Edgars NemšeAnnotator Disagreement Confidence Region Estimation
GenLayer Labs Research
BiG-SURE - Bipartite Graph for Semantic Uncertainty and Reliability Estimation of LLMs
Aug 31, 2026Debarpan Bhattacharya, Malay Phadke, Sriram GanapathyUncertainty Quantification LLM Uncertainty Estimation
LEAP Lab, Electrical Engineering, Indian Institute of Science · TANUH AI Centre of Excellence, Indian Institute of Science

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SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models

Xin-Yu Hu, Shuang Liang, Cheng Feng, Shao-Qun Zhang

National Key Laboratory for Novel Software Technology, Nanjing University, China. · School of Intelligent Science and Technology, Nanjing University, China. · Siemens Data and AI Research, Beijing, China.+1

Uncertainty Quantification Time Series Forecasting Time-Series Foundation Models

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The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predictions over extended future horizons. However, existing TSFMs often suffer from significantly inherent uncertainty, which typically manifests as derived forecast branches emerging at each time step and spreading to subsequent steps; different forecast branches often exhibit varying forecasting performance, thereby undermining the credibility of TSFM forecasts. In this paper, we propose the Slicing-Graphing-Alignment (SGA) method to quantify the uncertainty of multi-step TSFM forecasts. The proposed SGA first characterizes the topology of all potential forecast branches using a directed acyclic graph, such that the graph complexity bounds the uncertainty of multi-step forecasts, and then precisely measures the graph complexity by integrating both topological information and TSFM-inherent stochasticity. Experimental results conducted on 11 TSFMs and 27 datasets demonstrate that (i) SGA achieves the best performance when ranking predictive errors with uncertainty estimates; (ii) SGA works with a more extensive and more precise sampling coverage than those of existing UQ methods, deriving a quantification mechanism fundamentally different from those of established ones; and (iii) larger model scales of TSFMs correlate with lower uncertainty estimates of multi-step forecasts, suggesting another empirical scaling law for uncertainty quantification of multi-step TSFM forecasts.

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