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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Generative Inverse Design with Abstention via Diagonal Flow Matching
Mar 16, 2026Miguel de Campos, Werner Krebs, Hanno GottschalkFlow Matching Uncertainty Quantification
Institute of Mathematics, TU Berlin · Siemens Energy
Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification
Mar 10, 2026Junhyeok Lee, Minseo Choi, Han Jang +5Evidential Deep Learning Uncertainty Quantification
Cancer Biology, Seoul National University College of Medicine, Seoul, Korea · Radiology, Seoul National University Hospital, Seoul, Korea · Bioengineering, Seoul National University, Seoul, Korea +3
Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data
Mar 9, 2026L. Julián Lechuga López, Tim G. J. Rudner, Farah E. ShamoutBayesian Neural Networks Uncertainty Quantification
Tandon School of Engineering, New York University · New York University Abu Dhabi · University of Toronto
VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees
Feb 27, 2026Somjit Roy, Pritam Dey, Bani K. MallickUncertainty Quantification Symbolic Regression
Department of Statistics, Texas A&M University, College Station, Texas, USA
Semantic Self-Distillation for Language Model Uncertainty
Feb 4, 2026Edward Phillips, Sean Wu, Fredrik K. Gustafsson +2Uncertainty Quantification Confidence Estimation in Language Models
Department of Engineering Science, University of Oxford · Oxford Suzhou Centre for Advanced Research, University of Oxford, Suzhou
Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)
Feb 3, 2026Alex Finkelstein, Ron Moneta, Or Zohar +4Magnetic Resonance Imaging Uncertainty Quantification
School of Biomedical Engineering, Tel-Aviv University, Tel-Aviv, Israel · Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel · School of Biochemistry, Neurobiology, and Biophysics, Tel-Aviv University, Tel-Aviv, Israel +1
Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification
Feb 3, 2026Jack Michael Solomon, Rishi Leburu, Matthias ChungVariational Autoencoders Uncertainty Quantification
Emory University Department of Mathematics
Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction
Jan 19, 2026Ilias I. Giannakopoulos, Lokesh B Gautham Muthukumar, Yvonne W. Lui +1Uncertainty Quantification Accelerated MRI Reconstruction
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, NYU Grossman School of Medicine, 10016, New York, NY, United States of America · Courant Institute of Mathematical Sciences, NYU, 10012, New York, NY, United States of America · Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Grossman School of Medicine, 10016, New York, NY, United States of America
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
Jan 12, 2026Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab MukherjeeUncertainty Quantification Amortized Inference
Department of Statistics and Data Science, Indian Institute of Technology Kanpur, Kanpur, India 208016 · Marshall School of Business, University of Southern California, Los Angeles, United States 90089-0809
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
Quantitative Understanding of PDF Fits and their Uncertainties
Dec 30, 2025Amedeo Chiefa, Luigi Del Debbio, Richard KenwayUncertainty Quantification High-Energy Physics
The Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh, Peter Guthrie Tait Road, Edinburgh EH9 3FD, United Kingdom
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
Beyond Uncertainty Sets: Leveraging Optimal Transport to Extend Conformal Predictive Distributions to Multivariate Settings
Nov 19, 2025Eugene NdiayeUncertainty Quantification Conformal Prediction
Apple
QiVC-Net: Quantum-Inspired Variational Convolutional Network, with Application to Biosignal Classification
Nov 7, 2025Amin Golnari, Jamileh Yousefi, Reza Moheimani +1Uncertainty Quantification Quantum-Inspired ML
College of Engineering and Computer Science VinUniversity Hanoi, Vietnam · Shannon School of Business Cape Breton University Sydney, NS, Canada · Faculty of Computer Science Chemnitz University of Technology Chemnitz, Germany +2
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
Oct 29, 2025Christopher Bülte, Yusuf Sale, Gitta Kutyniok +1Uncertainty Quantification Aleatoric Uncertainty
LMU Munich · 2Munich Center for Machine Learning (MCML) · 3DLR-German Aerospace Center +2
Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC Codes
Oct 5, 2025Xiangjun Mi, Frank MuellerOOD Generalization Uncertainty Quantification
North Carolina State University, Raleigh, NC 27695, USA
Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Sep 17, 2025Edward Phillips, Sean Wu, Soheila Molaei +3Uncertainty Quantification LLM Hallucination Mitigation
Department of Engineering Science, University of Oxford · GlaxoSmithKline · Oxford Suzhou Centre for Advanced Research
Uncertainty-Aware Ankle Exoskeleton Control
Aug 28, 2025Fatima Mumtaza Tourk, Bishoy Galoaa, Sanat Shajan +3Uncertainty Quantification Ankle Exoskeleton Control
Department of Mechanical Engineering, Northeastern University, Boston, MA 02115 USA
DE-VAE: Revealing Uncertainty in Parametric and Inverse Projections with Variational Autoencoders using Differential Entropy
Aug 16, 2025Frederik L. Dennig, Daniel A. KeimVariational Autoencoders Uncertainty Quantification
University of Konstanz
A General Approach to Visualizing Uncertainty in Statistical Graphics
Jul 31, 2025Bernarda Petek, David Nabergoj, Erik ŠtrumbeljScientific Visualization Data Visualization
Last Layer Hamiltonian Monte Carlo
Jul 11, 2025Koen Vellenga, H. Joe Steinhauer, Göran Falkman +2Bayesian Neural Networks Markov Chain Monte Carlo
University of Skövde & Volvo Car Corporation, Sweden · University of Skövde, Sweden · Volvo Car Corporation, Sweden
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
Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification
Jun 23, 2025Ciro Listone, Aniello MuranoVariational Autoencoders Skin Lesion Classification
University of Naples Federico II, Naples, Italy.
Representation Learning for Equivariant Inference with Guarantees
May 26, 2025Daniel Ordoñez-Apraez, Vladimir Kostić, Alek Fröhlich +3Disentangled Representation Learning Representation Learning
CSML, Italian Institute of Technology, Genova, Italy · Universit`a di Genova, Genova, Italy · University of Novi Sad, Novi Sad, Serbia +2
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
Apr 25, 2025Christopher Bülte, Yusuf Sale, Timo Löhr +3Uncertainty Quantification Aleatoric Uncertainty
1LMU Munich · 2Munich Center for Machine Learning (MCML) · 3DLR-German Aerospace Center +2
Bifidelity Parameter Estimation Using Conditional Diffusion Models
Apr 2, 2025Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1Simulation-Based Inference Surrogate Modeling
Department of Mathematics, The Ohio State University, Columbus, OH 43210, USA · Fusion Energy Science Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA · Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
Mar 13, 2025Eirik Høyheim, Lars Skaaret-Lund, Solve Sæbø +1Bayesian Neural Networks Uncertainty Quantification
Bioinformatics and applied statistics (BIAS), Norwegian University of Life Sciences, Ås, Norway
Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs
Feb 1, 2025Bongseok Kim, Haoyang Zheng, Michael Penwarden +1Dynamical Systems Surrogate Modeling
School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, 47907, IN, USA. · Department of Mathematics, Purdue University, 150 North University Street, West Lafayette, 47907, IN, USA.
Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation
Dec 10, 2024Qinhong Lin, Yinglun Feng, Yuhao Zhang +2Uncertainty Quantification LLM Uncertainty Estimation
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China · QuanCheng Laboratory, Jinan, China
Uncertainty measurement for complex event prediction in safety-critical systems
Nov 2, 2024Maria J. P. Peixoto, Akramul AzimUncertainty Quantification Sensitivity Analysis
Department of Electrical, Computer and Software Engineering, Ontario Tech University, Oshawa, Ontario, Canada.

Mar 16, 2026, cs.LGJ/K move · Enter open · S save

Generative Inverse Design with Abstention via Diagonal Flow Matching

Miguel de Campos, Werner Krebs, Hanno Gottschalk

Institute of Mathematics, TU Berlin · Siemens Energy

Flow Matching Uncertainty Quantification Flow-Based Generative Modeling Inverse Design

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Inverse design aims to find design parameters

x

achieving target performance

y^*

. Generative approaches learn bidirectional mappings between designs and labels, enabling diverse solution sampling. However, standard conditional flow matching (CFM), when adapted to inverse problems by pairing labels with design parameters, exhibits strong sensitivity to their arbitrary ordering and scaling, leading to unstable training. We introduce Diagonal Flow Matching (Diag--CFM), which resolves this through a zero-anchoring strategy that pairs design coordinates with noise and labels with zero, making the learning problem provably invariant to coordinate permutations. This yields substantially lower round-trip error than CFM and invertible neural network baselines across design dimensions up to

P{=}784

, including order-of-magnitude gains on several benchmarks. We develop two architecture-intrinsic uncertainty metrics, Zero-Deviation and Self-Consistency, that enable three practical capabilities: selecting the best candidate among multiple generations, abstaining from unreliable predictions, and detecting out-of-distribution targets; consistently outperforming ensemble and general-purpose alternatives across all tasks. We validate on airfoil, gas turbine combustor, scalable analytical benchmarks, a photonics inverse-design task, and an image-statistics benchmark.

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Latest papers 431