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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Safety-Critical Camera Reliability Monitoring for ADAS via Degradation-Aware Uncertainty Pattern Analysis
May 6, 2026Shiva AherUncertainty Quantification Autonomous Driving Perception
Georgia Institute of Technology, Atlanta, GA, USA.
The First Token Knows: Single-Decode Confidence for Hallucination Detection
May 6, 2026Mina GabrielUncertainty Quantification Self-Consistency Decoding
Department of Computer and Information Sciences Temple University Philadelphia, PA 19122, USA
Beyond Semantics: An Evidential Reasoning-Aware Multi-View Learning Framework for Trustworthy Mental Health Prediction
May 6, 2026Yucheng Ruan, Ling Huang, Qika Lin +2Evidential Deep Learning Uncertainty Quantification
Saw Swee Hock School of Public Health, National University of Singapore, Singapore
Quantile-Free Uncertainty Quantification in Graph Neural Networks
May 6, 2026Soyoung park, Hwanjun Song, Sungsu LimGraph Neural Networks Uncertainty Quantification
Department of Computer Science and Engineering, Chungnam National University, Daejeon, Korea · Department of Industrial and Systems Engineering, KAIST, Daejeon, Korea
Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models
May 6, 2026Mingda Li, Rundong Lv, Xinyu Li +2Uncertainty Quantification LLM Uncertainty Estimation
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, Harbin, China · Suzhou Research Institute, Harbin Institute of Technology, Harbin, China
Probabilistic Classification and Uncertainty Quantification of Sahara Desert Climate Using Feedforward Neural Networks
May 5, 2026Stephen Tivenan, Indranil Sahoo, Yanjun QianUncertainty Quantification Feedforward Neural Networks
Department of Statistical Sciences and Operations Research, Virginia Commonwealth University
Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification
May 5, 2026Nan Feng, Xun HuanUncertainty Quantification Amortized Inference
Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, United States
PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics
May 5, 2026Hao Zhou, Rui Zhang, Han Wan +1Physics-Informed Generative Modeling Uncertainty Quantification
Gaoling School of Artificial Intelligence, Renmin University of China
PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution
May 5, 2026Onkar Jadhav, Tim French, Matthew Rayson +1Uncertainty Quantification Proper Orthogonal Decomposition
School of Earth and Oceans, UWA Oceans Institute, University of Western Australia, Crawley, WA, Australia · School of Physics, Mathematics and Computing, Computer Science and Software Engineering, University of Western Australia, Crawley, WA, Australia.
On Surprising Effects of Risk-Aware Domain Randomization for Contact-Rich Sampling-based Predictive Control
May 5, 2026Sergio A. Esteban, Junheng Li, Vince Kurtz +1Domain Randomization Contact-Rich Manipulation
Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, USA · School of Computing, DePaul University, Chicago, IL, USA
Boundary-Aware Uncertainty Quantification for Wildfire Spread Prediction
May 4, 2026Jonas V. FunkUncertainty Quantification Wildfire Forecasting
Independent Research 76199 Karlsruhe, Germany
AutoFocus: Uncertainty-Aware Active Visual Search for GUI Grounding
May 4, 2026Ruilin Yao, Shegnwu Xiong, Tianyu Zou +2Uncertainty Quantification GUI Grounding
Wuhan University of Technology
Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
May 3, 2026Qiao LiuIncomplete Data Imputation Uncertainty Quantification
Department of Biostatistics Yale University
RADMI: Latent Information Aggregation as a Proxy for Model Uncertainty
May 2, 2026William Stevens, Mohit Prabhushankar, Ghassan AlRegibUncertainty Quantification Dense Prediction
OLIVES at the Center for Signal and Information Processing, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
SCALE-LoRA: Auditing Post-Retrieval LoRA Composition with Residual Merging and View Reliability
May 2, 2026Shuaipeng Zhou, Yu ZhangUncertainty Quantification Adapter Tuning
School of Computer Science and Technology, Shenyang University of Chemical Technology · 2Liaoning Key Laboratory of Intelligent Technology for Chemical Process Industry
Robust Parameter Learning for Uncertain MDPs
May 2, 2026Yannik Schnitzer, Alessandro Abate, David ParkerRobust Markov Decision Processes Parameter Estimation
University of Oxford, Oxford, United Kingdom
Uncertainty-Aware Trip Purpose Inference from GPS Trajectories via POI Semantic Zones and Pareto Calibration
May 2, 2026Bo Yang, Haoxuan Ma, Yifan Liu +4Uncertainty Quantification Weakly Supervised Learning
1UCLA Mobility Lab, Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, USA. · 2Novateur Research Solutions, Ashburn, VA, USA.
Deep Variational Inference Symbolic Regression
May 1, 2026James Butterworth, Gevik Grigorian, Alejandro DiazDelaOUncertainty Quantification Variational Inference
Clinical Operational Research Unit University College London London, United Kingdom · Department of Computer Science University College London London, United Kingdom
Possibilistic Predictive Uncertainty for Deep Learning
May 1, 2026Yao Ni, Jeremie Houssineau, Yew-Soon Ong +1Uncertainty Quantification Epistemic Uncertainty
Nanyang Technological University · A*STAR · The University of New South Wales +1
Divergence is Uncertainty: A Closed-Form Posterior Covariance for Flow Matching
May 1, 2026Jiarui Xing, Song Wang, Jian WangFlow Matching Uncertainty Quantification
Yale University · Harvard Medical School
Conformalized Quantum DeepONet Ensembles: Towards Scalable Operator Learning with Distribution-Free Guarantees
May 1, 2026Purav Matlia, Christian Moya, Guang LinUncertainty Quantification Hybrid Quantum-Classical ML
Department of Computer Science Purdue University · Department of Mathematics Purdue University · Department of Mathematics Department of Mechanical Engineering Purdue University
Adaptive Geodesic Conformal Prediction for Egocentric Camera Pose Estimation
Apr 30, 2026Aishani Pathak, Hasti SeifiUncertainty Quantification Camera Pose Estimation
Arizona State University
Confidence Estimation in Automatic Short Answer Grading with LLMs
Apr 30, 2026Longwei Cong, Sonja Hahn, Sebastian Gombert +3Uncertainty Quantification Selective Prediction
DIPF | Leibniz Institute for Research and Information in Education, 60323 Frankfurt am Main, Germany · Faculty of Computer Science, Goethe University Frankfurt, 60629 Frankfurt am Main, Germany · Chemnitz University of Technology, 09111 Chemnitz, Germany
Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments
Apr 30, 2026Akhil Gupta, Erhan GuvenBayesian Neural Networks Kalman Filtering
Whiting School of Engineering EP Program, Johns Hopkins University, Baltimore, Maryland, USA
Geometry-Calibrated Conformal Abstention for Language Models
Apr 30, 2026Rui Xu, Yi Chen, Sihong Xie +1Uncertainty Quantification Selective Prediction
Information Hub, AI Thrust Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China
Prediction-powered Inference by Mixture of Experts
Apr 30, 2026Yanwu Gu, Linglong Kong, Dong XiaUncertainty Quantification Mixture-of-Experts Models
Department of Mathematics, Hong Kong University of Science and Technology · Department of Mathematical and Statistical Sciences, University of Alberta
Real-Time GPU-Accelerated Monte Carlo Evaluation of Safety-Critical AEB Systems Under Uncertainty
Apr 29, 2026Akshay Karjol, Shadi AlawnehCollision Avoidance Uncertainty Quantification
Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309 USA
ConformaDecompose: Explaining Uncertainty via Calibration Localization
Apr 29, 2026Fatima Rabia Yapicioglu, Meltem Aksoy, Alberto Rigenti +4Uncertainty Quantification Conformal Prediction
Department of Computer Science and Engineering, University of Bologna, Italy · Marketing and Sales, Automobili Lamborghini S.p.A, Sant’Agata Bolognese, Italy · RCT Data Science and Security, Technical University Dortmund, Germany +4
Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics
Apr 29, 2026Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank +2Uncertainty Quantification Model Predictive Control
Institute for Data Science in Mechanical Engineering (DSME), RWTH Aachen University · Institute of Climate and Energy Systems (ICE), Energy Systems Engineering (ICE-1), Forschungszentrum Jülich GmbH
Laplace Approximation for Bayesian Tensor Network Kernel Machines
Apr 29, 2026Albert Saiapin, Kim BatselierUncertainty Quantification Bayesian Inference
1Delft Center for Systems and Control, Delft University of Technology

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

Safety-Critical Camera Reliability Monitoring for ADAS via Degradation-Aware Uncertainty Pattern Analysis

Shiva Aher

Georgia Institute of Technology, Atlanta, GA, USA.

Uncertainty Quantification Autonomous Driving Perception

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Reliable camera input is essential for safety-critical ADAS perception, but most monitoring approaches detect sensor failures only after downstream performance has degraded. We propose a proactive camera reliability monitoring framework that estimates perception risk from degradation-induced uncertainty patterns before downstream failure becomes observable. The method introduces a Global Sensor Health Index (GSHI), a continuous reliability score that aggregates per-degradation severities using a risk-aware multiplicative formulation, allowing severe single-mode failures such as lens occlusion or motion blur to dominate the health estimate. A lightweight multi-task network predicts degradation type, severity, GSHI, and spatial uncertainty maps from a single RGB image without downstream task feedback. Training uses physics- and geometry-aware synthetic supervision over twelve camera degradation modes. Experiments on KITTI-derived degradations show that GSHI decreases monotonically with severity, achieves a health-estimation MAE of 0.064, and provides positive early-warning lead time of 0.47

\pm

0.25 severity units before YOLOv8 detection failure. GSHI also outperforms IQA, detector-confidence, and clean-feature OOD baselines, and transfers zero-shot to real adverse-weather driving data. These results support degradation-aware uncertainty analysis as a practical direction for proactive camera reliability monitoring in intelligent vehicles.

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