Uncertainty Quantification

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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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  1. Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network

    Jun 19, 2026Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie +1Uncertainty QuantificationBayesian Inverse Problems

    Japan Agency for Marine-Earth Science and Technology · Earth Science New Zealand

  2. Embedding Linear Equality Constraints in Probabilistic Neural Networks for Dynamic Modelling

    Jun 19, 2026Matthew Marsh, Benoit Chachuat, Antonio del Rio ChanonaNeural Surrogate ModelingBayesian Neural Networks

    The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK

  3. Rejections Based on Predictive Uncertainty Enable Reliable Routine Soil Spectroscopy

    Jun 19, 2026Jonas Schmidinger, Robin Gebbers, Marc-Olivier Gasser +3Uncertainty QuantificationSelective Prediction

    Joint Lab Artificial Intelligence and Data Science, Osnabrück University, Osnabrück, Germany. · Department of Agromechatronics, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany. · Research and Development Institute for the Agri-environment (IRDA), Qu´ebec City, Canada. +1

  4. Continuous-Time Probabilistic Correctors for Uncertainty-Aware Physics-Based Spacecraft Trajectory Forecasting

    Jun 19, 2026Muhammad Bilal Shahid, Zhanhong Jiang, Soumik Sarkar +1Neural Controlled Differential EquationsUncertainty Quantification

    Iowa State University, Ames, Iowa, 50011, USA

  5. Stochastic Signed Distance Processes

    Jun 18, 2026Hiroki Sakuma, Masatoshi OkutomiUncertainty QuantificationNeural Rendering

    Department of Systems and Control Engineering Institute of Science Tokyo

  6. Neural network surrogates with uncertainty quantification for inverse problems in partial differential equations

    Jun 18, 2026Christian Jimenez-Beltran, Aretha L. Teckentrup, Antonio Vergari +1Neural Surrogate ModelingUncertainty Quantification

    School of Mathematics and Maxwell Institute for Mathematical Sciences University of Edinburgh · School of Informatics University of Edinburgh

  7. Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities

    Jun 18, 2026Yucheng Xing, Hailan Mo, Zi Wang +2Missing-Modality LearningUncertainty Quantification

    National University of Singapore, Singapore · Imperial College London, London, UK

  8. A Systematic Evaluation of Black-Box Uncertainty Estimation Methods for Large Language Models

    Jun 18, 2026Jiayi Wang, Xu-Yao ZhangLLM EvaluationUncertainty Quantification

    School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

  9. On the QUEST for Uncertainty Quantification via Highest Density Regions

    Jun 17, 2026Sam Goring, Tom Kuipers, Nicola Paoletti +1Uncertainty QuantificationAleatoric Uncertainty

    King’s College London · Northeastern University London

  10. Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation

    Jun 17, 2026Hana Jebril, Thomas Pinetz, Günter Klambauer +1Uncertainty QuantificationMedical Image Segmentation

    Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria · Comprehensive Center for AI in Medicine, Medical University of Vienna, Austria · ELLIS Unit Linz, LIT AI Lab and Institute for Machine Learning, Johannes Kepler University Linz, Austria +1

  11. Geometry-Aware Post-Hoc Uncertainty Quantification in Operator Learning

    Jun 16, 2026Oriol Vendrell-Gallart, Nima Negarandeh, Ramin BostanabadUncertainty QuantificationPDE Surrogate Modeling

    Department of Mechanical and Aerospace Engineering University of California, Irvine Irvine, CA 92617

  12. Bounded Difference Concentration for Infinitely Exchangeable Sequences with Applications to AI Benchmark Uncertainty

    Jun 16, 2026Fangyuan Lin, Spencer Frei, Victor H. de la PenaUncertainty Quantification

    Department of Statistics, Columbia University · Google DeepMind

  13. Trustworthy MRI Reconstruction via Bayesian Uncertainty Quantification with Sparsity Prior Models

    Jun 15, 2026Ahmed Karam Eldaly, Matteo Figini, Daniel C. AlexanderSparse RecoveryUncertainty Quantification

    Department of Computer Science, University of Exeter, Exeter EX4 4QF, U.K. · UCL Hawkes Institute, Department of Computer Science, University College London, London, U.K.

  14. Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning

    Jun 15, 2026Qitian Lu, Jafar Jafari-Asl, Panagiotis Spyridis +1Surrogate ModelingUncertainty Quantification

    Brno University of Technology, Brno, Czech Republic · University of Rostock, Rostock, Germany

  15. Simulation-Augmented Multi-Step Split Conformal Prediction for Aggregated Forecasts

    Jun 15, 2026Andro SabashviliUncertainty QuantificationTime Series Forecasting

  16. Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning

    Jun 15, 2026Tobias Jan Wieczorek, Leon de Andrade, Thomas Möllenhoff +1Bayesian Neural NetworksUncertainty Quantification

    TU Darmstadt & hessian.AI, Darmstadt, Germany · RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

  17. Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning

    Jun 14, 2026Dong Hyun Jeong, Feng Chen, Jin-Hee Cho +3Uncertainty Quantification

    University of the District of Columbia Department of Computer Science Washington, DC, USA · University of Texas at Dallas Department of Computer Science Richardson, TX, USA · Virginia Tech Department of Computer Science Blacksburg, VA, USA +3

  18. Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling

    Jun 14, 2026Yuhan Wang, Manqing Wang, Yixuan Lu +2Uncertainty QuantificationDempster-Shafer Theory

    Columbia University New York, USA · Trine University Phoenix, USA · University of Sofia Palo Alto, USA +2

  19. Bayesian 3D Steerable CNNs: Enabling Equivariance and Uncertainty Quantification Simultaneously

    Jun 13, 2026Abhishek Keripale, Ponkrshnan Thiagarajan, Susanta GhoshBayesian Neural NetworksUncertainty Quantification

    Department of Mechanical and Aerospace Engineering, Michigan Technological University, Houghton, MI 49931 · Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD 21218 · The Center for Artificial Intelligence at the Institute of Computing and Cybersystems, Michigan Technological University, USA

  20. Audited Conformal Prediction for Classification under Unknown Distribution Shift

    Jun 12, 2026Yanfei Zhou, Rizal Fathony, Nam H. Nguyen +1Distribution Shift RobustnessUncertainty Quantification

    Department of Data Sciences and Operations University of Southern California Los Angeles, CA 90089 · AI Foundations, Capital One New York, NY 10017 · Department of Data Sciences and Operations Thomas Lord Department of Computer Science University of Southern California Los Angeles, CA 90089

  21. Hybrid Uncertainty Sensitivity Analysis Based on the HSIC for High-Dimensional Responses with Aleatory--Epistemic Separation

    Jun 12, 2026Shijie Zhong, Jiangfeng Fu, Pengfei WeiUncertainty QuantificationAleatoric Uncertainty

    School of Power and Energy Northwestern Polytechnical University Xi’an, Shanxi 710129

  22. Uncertainty Estimation and Generalization Bounds for Modern Deep Learning

    Jun 11, 2026Luis A. OrtegaBayesian Neural NetworksNeural Network Generalization

    Department of Computer Science Machine Learning Group Madrid

  23. Epistemic Uncertainty Is Not the Reducible Kind

    Jun 10, 2026Robin YoungUncertainty QuantificationUncertainty Decomposition

    University of Cambridge

  24. What Uncertainties Do We Need for Dynamical Systems?

    Jun 10, 2026Yusuf Sale, Christopher Bülte, Felix Czaja +2Dynamical SystemsUncertainty Quantification

    Institute of Computer Science, LMU Munich · Munich · Department of Mathematics, LMU Munich +1

  25. Modelling magnetic material properties with uncertainty-aware neural networks

    Jun 10, 2026Clemens Wager, Heisam Moustafa, Alexander Kovacs +10Uncertainty QuantificationMaterials Property Prediction

    Christian Doppler Laboratory for magnet design through physics informed machine learning, University for Continuing Education Krems, Wr. Neustadt, 2700, Austria · Vienna Doctoral School in Physics, Vienna, Austria · Department for integrated sensor systems, University for Continuing Education Krems, Wr. Neustadt, 2700, Austria +1

  26. Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification

    Jun 10, 2026Handi Zhang, Adrienne M. Propp, Brooks Kinch +2Uncertainty QuantificationPDE Surrogate Modeling

    Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA · Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA · Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA +1

  27. Confidence Laundering in Agent Systems: Why Uncertainty Needs a Latent Carrier

    Jun 9, 2026Kaiwen Shi, Zheyuan Zhang, Han Bao +2Agent ReliabilityUncertainty Quantification

    University of Notre Dame

  28. An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer

    Jun 9, 2026Cedric Hemon, Delphine Lebret, Jean-Claude Nunes +8Uncertainty QuantificationOncology

    Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France · Department of Radiation Oncology, Centre Georges-Francois Leclerc, F-21000 Dijon, France · Institut de Cancérologie de l’Ouest–Site Paul Papin, F-49000 Angers, France +1

  29. Can we trust our models? Epistemic calibration in second-order classification

    Jun 9, 2026Arthur HoarauUncertainty QuantificationUncertainty Calibration

    Université de Lorraine, CentraleSupélec · Loria, CNRS, Metz, France

  30. Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions

    Jun 8, 2026Ludvig Doeser, Jens JascheSimulation-Based InferenceNormalizing Flows

    The Oskar Klein Centre, Department of Physics, Stockholm University, AlbaNova University Centre, SE 106 91 Stockholm, Sweden · Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, NY 10010, USA