Uncertainty Quantification

ICLR 2025arXiv:2305.19187

#2

Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models

Jimeng Sun, Shubhendu Trivedi, Zhen Lin

248

ICML 2024arXiv:2311.08718

#3

Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling

Bairu Hou, Yujian Liu, Kaizhi Qian et al.

uncertainty decompositionaleatoric uncertaintyepistemic uncertaintylarge language models+3

101

CVPR 2024arXiv:2309.03185

#4

Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields

Leili Goli, Cody Reading, Silvia Sellán et al.

92

ICML 2024arXiv:2402.03563

#5

Distinguishing the Knowable from the Unknowable with Language Models

Gustaf Ahdritz, Tian Qin, Nikhil Vyas et al.

epistemic uncertaintyaleatoric uncertaintylarge language modelsconfidence estimation+3

41

ICML 2024arXiv:2312.00995

#6

Second-Order Uncertainty Quantification: A Distance-Based Approach

Yusuf Sale, Viktor Bengs, Michele Caprio et al.

uncertainty quantificationsecond-order probability distributionspredictive uncertaintyclassification setting+2

33

ICLR 2024arXiv:2401.08501

#7

ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation

Kim-Celine Kahl, Carsten Lüth, Maximilian Zenk et al.

23

ICML 2025arXiv:2505.22655

#8

Position: Uncertainty Quantification Needs Reassessment for Large Language Model Agents

Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci

20

ICML 2025arXiv:2412.20892

#9

Rethinking Aleatoric and Epistemic Uncertainty

Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope et al.

20

ECCV 2024arXiv:2403.07263

#10

Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction

Alexander Timans, Christoph-Nikolas Straehle, Kaspar Sakmann et al.

conformal predictionuncertainty quantificationobject detectionbounding box localization+4

19

ICLR 2025arXiv:2402.10727

#11

From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation

Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč et al.

predictive uncertaintybayesian estimationaleatoric uncertaintyepistemic uncertainty+3

17

ICLR 2025arXiv:2502.10709

#12

An Empirical Analysis of Uncertainty in Large Language Model Evaluations

Qiujie Xie, Qingqiu Li, Zhuohao Yu et al.

llm-as-a-judgeevaluation uncertaintymodel-based evaluationprompting strategies+4

16

AAAI 2024arXiv:2312.10469

#13

One Step Closer to Unbiased Aleatoric Uncertainty Estimation

Wang Zhang, Ziwen Martin Ma, Subhro Das et al.

aleatoric uncertainty estimationepistemic uncertaintyvariance attenuationdata de-noising+2

AAAI 2025arXiv:2410.08985

#14

Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective

Bo Ni, Yu Wang, Lu Cheng et al.

knowledge graph reasoninguncertainty quantificationconformal predictionlarge language models+3

ICML 2024arXiv:2309.10852

#15

Using AI Uncertainty Quantification to Improve Human Decision-Making

Laura Marusich, Jonathan Bakdash, Yan Zhou et al.

uncertainty quantificationhuman decision-makingprobabilistic informationmodel explainability+3

Uncertainty QuantificationLLM ExplanationsGraph Mining

#16

Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology

Longchao Da, Xiaoou Liu, Jiaxin Dai et al.

COLM 2025

ICML 2024arXiv:2310.11558

#17

Online Algorithms with Uncertainty-Quantified Predictions

Bo Sun, Jerry Huang, Nicolas Christianson et al.

algorithms with predictionsuncertainty quantificationonline algorithmsski rental problem+3

COLM 2025arXiv:2503.19168

#18

Language Model Uncertainty Quantification with Attention Chain

Yinghao Li, Rushi Qiang, Lama Moukheiber et al.

Uncertainty EstimationLarge Language ModelAttention

10

ICML 2024arXiv:2302.06495

#19

Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts

Ha Manh Bui, Anqi Liu

uncertainty estimationdistribution shiftslipschitz-constrained feature extractordistance-aware models+3

CVPR 2024arXiv:2404.03789

#20

Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture

Juanwu Lu, Can Cui, Yunsheng Ma et al.

quantum state estimationdiffusion modelsquantum many-body problemsentanglement entropy+2

#21

QuaDiM: A Conditional Diffusion Model For Quantum State Property Estimation

Yehui Tang, Mabiao Long, Junchi Yan

ICLR 2025

ICLR 2024arXiv:2402.08324

#22

Uncertainty Quantification via Stable Distribution Propagation

Felix Petersen, Aashwin Mishra, Hilde Kuehne et al.

AAAI 2024arXiv:2308.09065

#23

Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression

Xuanlong Yu, Gianni Franchi, Jindong Gu et al.

uncertainty quantificationauxiliary uncertainty estimatoraleatoric uncertaintyepistemic uncertainty+4

8

AAAI 2024arXiv:2402.08383

#24

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution

Tailin Wu, Willie Neiswanger, Hongtao Zheng et al.

8

AAAI 2025arXiv:2408.12970

#25

SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning

Zhongjian Qiao, Jiafei Lyu, Kechen Jiao et al.

8

NEURIPS 2025arXiv:2502.02870

#26

Uncertainty Quantification with the Empirical Neural Tangent Kernel

Joseph Wilson, Chris van der Heide, Liam Hodgkinson et al.

uncertainty quantificationneural tangent kernelbayesian methodsdeep ensembles+3

7

ICLR 2024arXiv:2401.03350

#27

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks

Puja Trivedi, Mark Heimann, Rushil Anirudh et al.

7

CVPR 2024arXiv:2404.10124

#28

Epistemic Uncertainty Quantification For Pre-Trained Neural Networks

Hanjing Wang, Qiang Ji

uncertainty quantificationepistemic uncertaintygradient-based methodsout-of-distribution detection+4

7

AAAI 2024arXiv:2312.10435

#29

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

Shunsuke Horii, Yoichi Chikahara

heterogeneous treatment effectsuncertainty quantificationbayesian inferencegaussian process priors+4

6

ICLR 2025arXiv:2412.18808

#30

Provable Uncertainty Decomposition via Higher-Order Calibration

Gustaf Ahdritz, Aravind Gollakota, Parikshit Gopalan et al.

6

ICLR 2025arXiv:2410.04315

#31

Calibrating Expressions of Certainty

Peiqi Wang, Barbara Lam, Yingcheng Liu et al.

certainty calibrationlinguistic expressionsuncertainty distributionspost-hoc calibration+3

5

ECCV 2024arXiv:2405.18527

#32

Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction

Jeffrey Wen, Rizwan Ahmad, Phillip Schniter

inverse problemsuncertainty quantificationconformal predictionaccelerated mri+3

5

CVPR 2025arXiv:2412.03178

#33

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation

Gianni Franchi, Nacim Belkhir, Dat NGUYEN et al.

uncertainty quantificationtext-to-image generationlarge vision-language modelsaleatoric epistemic uncertainty+4

5

ICML 2025arXiv:2505.05049

#34

UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model

Timo Kaiser, Thomas Norrenbrock, Bodo Rosenhahn

4

ICML 2024arXiv:2401.02413

#35

Simulation-Based Inference with Quantile Regression

He Jia

simulation-based inferencequantile regressionposterior estimationcredible region+4

4

COLM 2025arXiv:2505.01595

#36

Always Tell Me The Odds: Fine-grained Conditional Probability Estimation

Liaoyaqi Wang, Zhengping Jiang, Anqi Liu et al.

Large Language ModelProbabilistic ReasoningSemanticsCalibration

4

ICML 2024arXiv:2405.06424

#37

Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation

JoonHo Lee, Jae Oh Woo, Juree Seok et al.

instruction followinguncertainty estimationreward modelinglanguage model training+3

ICML 2024arXiv:2406.10775

#38

A Rate-Distortion View of Uncertainty Quantification

Ifigeneia Apostolopoulou, Benjamin Eysenbach, Frank Nielsen et al.

uncertainty quantificationinformation bottleneckout-of-distribution detectionprobabilistic models+3

ICML 2025arXiv:2502.04406

#39

Calibrated Physics-Informed Uncertainty Quantification

Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi et al.

ICLR 2025arXiv:2405.20986

#40

Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function

Linlin Yu, Bowen Yang, Tianhao Wang et al.

predictive uncertainty quantificationbird's eye view segmentationautonomous vehicle planningevidential deep learning+4

uncertainty quantificationregression analysiscontinuous ranked probability scoredistribution-free methods+4

#41

Distribution-Free Data Uncertainty for Neural Network Regression

Domokos M. Kelen, Ádám Jung, Péter Kersch et al.

ICLR 2025

CVPR 2025arXiv:2503.20011

#42

Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception

Luke Chen, Junyao Wang, Trier Mortlock et al.

NEURIPS 2025arXiv:2506.09684

#43

Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models

Haoyi Song, Ruihan Ji, Naichen Shi et al.

NEURIPS 2025arXiv:2505.19136

#44

Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference

Frank Shih, Zhenghao Jiang, Faming Liang

NEURIPS 2025arXiv:2509.02327

#45

Variational Uncertainty Decomposition for In-Context Learning

I. Shavindra Jayasekera, Jacob Si, Filippo Valdettaro et al.

in-context learninguncertainty decompositionbayesian uncertainty estimationepistemic uncertainty+4

AAAI 2025arXiv:2406.12100

#46

CUQDS: Conformal Uncertainty Quantification Under Distribution Shift for Trajectory Prediction

Huiqun Huang, Sihong He, Fei Miao

trajectory predictionuncertainty quantificationdistribution shiftconformal prediction+4

AAAI 2025arXiv:2412.10570

#47

Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks

Giorgio Morales, John W. Sheppard

ICLR 2025arXiv:2406.12082

#48

Uncertainty modeling for fine-tuned implicit functions

Anna Susmelj, Mael Macuglia, Natasa Tagasovska et al.

neural radiance fieldsoccupancy networkssigned distance functionsuncertainty estimation+3

AAAI 2024arXiv:2312.11319

#49

Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation

ICLR 2025arXiv:2507.20272

#50

Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence

Dharmesh Tailor, Alvaro Correia, Eric Nalisnick et al.

AAAI 2025arXiv:2502.14698

#51

General Uncertainty Estimation with Delta Variances

Simon Schmitt, John Shawe-Taylor, Hado van Hasselt

uncertainty quantificationepistemic uncertaintydelta variancesneural network uncertainty+3

NEURIPS 2025arXiv:2502.06067

#52

Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Associations

David Burt, Renato Berlinghieri, Stephen Bates et al.

ICCV 2025arXiv:2507.18366

#53

Evidential Knowledge Distillation

Liangyu Xiang, Junyu Gao, Changsheng Xu

uncertainty quantificationknowledge distillationevidential learningdirichlet distribution+4

ICML 2025arXiv:2505.22152

#54

FLUE: Streamlined Uncertainty Estimation for Large Language Models

Shiqi Gao, Tianxiang Gong, Zijie Lin et al.

Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory

Dominik Fuchsgruber, Tom Wollschläger, Johannes Bordne et al.

ICML 2024arXiv:2406.02745

#56

Measuring Stochastic Data Complexity with Boltzmann Influence Functions

Nathan Ng, Roger Grosse, Marzyeh Ghassemi

uncertainty calibrationminimum description lengthpredictive normalized maximum likelihoodboltzmann influence functions+3

ICLR 2024arXiv:2311.11723

#57

Dual Energy-Based Model with Open-World Uncertainty Estimation for Out-of-distribution Detection

Qi Chen, Hu Ding

Leveraging Uncertainty Estimates To Improve Classifier Performance

Gundeep Arora, Srujana Merugu, Anoop Saladi et al.

NEURIPS 2025arXiv:2506.18283

#59

Quantifying Uncertainty in the Presence of Distribution Shifts

Yuli Slavutsky, David Blei

uncertainty estimationcovariate shiftbayesian frameworkadaptive prior+3

AAAI 2025arXiv:2412.08513

#60

REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability

Kristoffer K. Wickstrøm, Thea Brüsch, Michael C. Kampffmeyer et al.

uncertainty estimationrepresentation learning explainabilityimportance score variabilitybernoulli random variables+3

ICML 2024arXiv:2406.06968

#61

Beyond the Norms: Detecting Prediction Errors in Regression Models

Andres Altieri, Marco Romanelli, Georg Pichler et al.

uncertainty quantificationregression error detectionprobabilistic modelingaleatoric uncertainty+4

uncertainty quantificationdeep neural networkscomputer visionnatural language processing+4

#62

Torch-Uncertainty: Deep Learning Uncertainty Quantification

Adrien Lafage, Olivier Laurent, Firas Gabetni et al.

NEURIPS 2025

NEURIPS 2025arXiv:2511.10439

#63

Online Differentially Private Conformal Prediction for Uncertainty Quantification

Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration

Thomas Decker, Volker Tresp, Florian Buettner

uncertainty calibrationperturbation-based explanationsexplanation robustnessmodel recalibration+2

NEURIPS 2025arXiv:2510.08044

#65

Towards Reliable LLM-based Robots Planning via Combined Uncertainty Estimation

Shiyuan Yin, Chenjia Bai, Zihao Zhang et al.

uncertainty estimationevidential deep learningdirichlet distributionsaleatoric uncertainty+4

#66

Unveiling the Uncertainty in Embodied and Operational Carbon of Large AI Models through a Probabilistic Carbon Accounting Model

Xiaoyang Zhang, He Fang, Yang Deng et al.

Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning

Pehuen Moure, Longbiao Cheng, Joachim Ott et al.

Self-Calibrating Vicinal Risk Minimisation for Model Calibration

Jiawei Liu, Changkun Ye, Ruikai Cui et al.

Vicinal Label Supervision for Reliable Aleatoric and Epistemic Uncertainty Estimation

Linye Li, Yufei Chen, Xiaodong Yue

NEURIPS 2025

NEURIPS 2025arXiv:2510.21310

#70

Efficient semantic uncertainty quantification in language models via diversity-steered sampling

Ji Won Park, Kyunghyun Cho

NEURIPS 2025arXiv:2505.11054

#71

NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification

Mélodie Monod, Alessandro Micheli, Samir Bhatt

survival analysisbayesian uncertainty quantificationtime-varying covariate-risk relationshipsvariational inference+3

NEURIPS 2025arXiv:2510.18322

#72

Uncertainty Estimation by Flexible Evidential Deep Learning

Taeseong Yoon, Heeyoung Kim

uncertainty quantificationevidential deep learningdirichlet distributionflexible dirichlet distribution+2

NEURIPS 2025arXiv:2512.00835

#73

Uncertainty Quantification for LLM-Based Survey Simulations

Chengpiao Huang, Yuhang Wu, Kaizheng Wang

QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions

Xinyu Yang, Tom Zollo, Benjamin Eyre et al.

Learning model uncertainty as variance-minimizing instance weights

Nishant Jain, Karthikeyan Shanmugam, Pradeep Shenoy

Uncertainty-Aware Yield Prediction with Multimodal Molecular Features

Jiayuan Chen, Kehan Guo, Zhen Liu et al.

CertainlyUncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness

Khyathi Chandu, Linjie Li, Anas Awadalla et al.

CoCoA: A Minimum Bayes Risk Framework Bridging Confidence and Consistency for Uncertainty Quantification in LLMs

Roman Vashurin, Maiya Goloburda, Albina Ilina et al.

Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows

Adriel Sosa Marco, John D. Kirwan, Alexia Toumpa et al.

uncertainty attributionbinary mask learninginformative perturbationssegment anything model+3

#80

Reexamining the Aleatoric and Epistemic Uncertainty Dichotomy

Michael Kirchhof, Gjergji Kasneci, Enkelejda Kasneci

Optimization-based Uncertainty Attribution Via Learning Informative Perturbations

Hanjing Wang, Bashirul Azam Biswas, Qiang Ji

ECCV 2024

uncertainty quantificationneural operatorspartial differential equationsout-of-domain learning+3

#82

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs

Chandra Mouli Sekar, Danielle Robinson, Shima Alizadeh et al.

ICML 2024

#83

QUTE: Quantifying Uncertainty in TinyML models with Early-exit-assisted ensembles for model-monitoring

Nikhil Pratap Ghanathe, Steve Wilton

ICML 2025