Uncertainty Quantification

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

Jimeng Sun, Shubhendu Trivedi, Zhen Lin

OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

Changhun Lee, Jungyu Jin, Taesu Kim et al.

Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields

Leili Goli, Cody Reading, Silvia Sellán et al.

Producing and Leveraging Online Map Uncertainty in Trajectory Prediction

Xunjiang Gu, Guanyu Song, Igor Gilitschenski et al.

On the Relation between Trainability and Dequantization of Variational Quantum Learning Models

Elies Gil-Fuster, Casper Gyurik, Adrian Perez-Salinas et al.

Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer

Wenqiao Zhang, Zheqi Lv

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

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

Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation

Konstantin Hess, Valentyn Melnychuk, Dennis Frauen et al.

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

Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč et al.

Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models

Gianni Franchi, Olivier Laurent, Maxence Leguéry et al.

Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modeling

Guiyu Zhang, Huan-ang Gao, Zijian Jiang et al.

Imputation for prediction: beware of diminishing returns.

Marine Le Morvan, Gael Varoquaux

Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model

Leheng Zhang, Weiyi You, Kexuan Shi et al.

R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning

Mengyuan Chen, Junyu Gao, Changsheng Xu

Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation

Hsiang Hsu, Guihong Li, Shaohan Hu et al.

Uncertainty Regularized Evidential Regression

Kai Ye, Tiejin Chen, Hua Wei et al.

One Step Closer to Unbiased Aleatoric Uncertainty Estimation

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

Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction

Wei Qian, Chenxu Zhao, Yangyi Li et al.

Variational Inference for SDEs Driven by Fractional Noise

Rembert Daems, Manfred Opper, Guillaume Crevecoeur et al.

Towards Trustworthy Knowledge Graph Reasoning: An Uncertainty Aware Perspective

Bo Ni, Yu Wang, Lu Cheng et al.

Evidential Uncertainty-Guided Mitochondria Segmentation for 3D EM Images

Ruohua Shi, Lingyu Duan, Tiejun Huang et al.

QuaDiM: A Conditional Diffusion Model For Quantum State Property Estimation

Yehui Tang, Mabiao Long, Junchi Yan

Error-quantified Conformal Inference for Time Series

Junxi Wu, Dongjian Hu, Yajie Bao et al.

Uncertainty-aware Graph-based Hyperspectral Image Classification

Linlin Yu, Yifei Lou, Feng Chen

Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression

Xuanlong Yu, Gianni Franchi, Jindong Gu et al.

Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback

Riccardo Della Vecchia, Debabrota Basu

Error Bounds for Gaussian Process Regression Under Bounded Support Noise with Applications to Safety Certification

Robert Reed, Luca Laurenti, Morteza Lahijanian

Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration

Zhihao Wang, Yulin Zhou, Ningyu Zhang et al.

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

Shunsuke Horii, Yoichi Chikahara

Epistemic Uncertainty Quantification For Pre-Trained Neural Networks

Hanjing Wang, Qiang Ji

Enhancing Uncertainty Modeling with Semantic Graph for Hallucination Detection

Kedi Chen, Qin Chen, Jie Zhou et al.

Calibrating Expressions of Certainty

Peiqi Wang, Barbara Lam, Yingcheng Liu et al.

Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability

Songyao Jin, Feng Xie, Guangyi Chen et al.

Feature Clipping for Uncertainty Calibration

Linwei Tao, Minjing Dong, Chang Xu

Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection

Pingting Hao, Kunpeng Liu, Wanfu Gao

Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction

Jeffrey Wen, Rizwan Ahmad, Phillip Schniter

Integral Imprecise Probability Metrics

Siu Lun (Alan) Chau, Michele Caprio, Krikamol Muandet

Uncertainty Quantification with the Empirical Neural Tangent Kernel

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

Nested Expectations with Kernel Quadrature

Zonghao Chen, Masha Naslidnyk, Francois-Xavier Briol

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation

David Heineman, Valentin Hofmann, Ian Magnusson et al.

Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates

Baozhen Wang, Xingye Qiao

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

Timo Kaiser, Thomas Norrenbrock, Bodo Rosenhahn

Non-parametric Sensor Noise Modeling and Synthesis

Ali Mosleh, Luxi Zhao, Atin Vikram Singh et al.

Monte Carlo Tree Search in the Presence of Transition Uncertainty

Farnaz Kohankhaki, Kiarash Aghakasiri, Hongming Zhang et al.

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

Gianni Franchi, Nacim Belkhir, Dat NGUYEN et al.

Maximum Entropy Model Correction in Reinforcement Learning

Amin Rakhsha, Mete Kemertas, Mohammad Ghavamzadeh et al.

On the Generalization of Representation Uncertainty in Earth Observation

Spyros Kondylatos, Nikolaos Ioannis Bountos, Dimitrios Michail et al.

Uncertainty Weighted Gradients for Model Calibration

Jinxu Lin, Linwei Tao, Minjing Dong et al.

RILQ: Rank-Insensitive LoRA-Based Quantization Error Compensation for Boosting 2-Bit Large Language Model Accuracy

Geonho Lee, Janghwan Lee, Sukjin Hong et al.

Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification

Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar et al.

Mind the Uncertainty in Human Disagreement: Evaluating Discrepancies Between Model Predictions and Human Responses in VQA

Jian Lan, Diego Frassinelli, Barbara Plank

Distribution-Free Data Uncertainty for Neural Network Regression

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

Uncertainty Visualization via Low-Dimensional Posterior Projections

Omer Yair, Tomer Michaeli, Elias Nehme

Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport

Tuan Dam, Pascal Stenger, Lukas Schneider et al.

Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models

Zeyu Zhou, Ruqi Bai, Sean Kulinski et al.

CUQDS: Conformal Uncertainty Quantification Under Distribution Shift for Trajectory Prediction

Huiqun Huang, Sihong He, Fei Miao

Calibrated Physics-Informed Uncertainty Quantification

Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi et al.

CBMA: Improving Conformal Prediction through Bayesian Model Averaging

Pankaj Bhagwat, Linglong Kong, Bei Jiang

Quantifying and Narrowing the Unknown: Interactive Text-to-Video Retrieval via Uncertainty Minimization

Bingqing Zhang, Zhuo Cao, Heming Du et al.

How Much is Unseen Depends Chiefly on Information About the Seen

Seongmin Lee, Marcel Boehme

Minimal Variance Model Aggregation: A principled, non-intrusive, and versatile integration of black box models

Theo Bourdais, Houman Owhadi

Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models

Xiao Liang, Di Wang, Zhicheng Jiao et al.

Credal Prediction based on Relative Likelihood

Timo Löhr, Paul Hofman, Felix Mohr et al.

Contextual Thompson Sampling via Generation of Missing Data

Kelly W Zhang, Tianhui Cai, Hongseok Namkoong et al.

Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models

Sima Noorani, Shayan Kiyani, George J. Pappas et al.

ViLU: Learning Vision-Language Uncertainties for Failure Prediction

Marc Lafon, Yannis Karmim, Julio Silva-Rodríguez et al.

Uncertainty-Aware Self-Training for CTC-Based Automatic Speech Recognition

Eungbeom Kim, Kyogu Lee

Dynamic Uncertainty Estimation for Offline Reinforcement Learning

Jiesheng Wang, Lin Li, Wei Wei et al.

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

Haoyi Song, Ruihan Ji, Naichen Shi et al.

Learning Likelihood-Free Reference Priors

Nick Bishop, Daniel Jarne Ornia, Joel Dyer et al.

Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations

Fred Xu, Thomas Markovich

FLUE: Streamlined Uncertainty Estimation for Large Language Models

Shiqi Gao, Tianxiang Gong, Zijie Lin et al.

Approximate Bilevel Difference Convex Programming for Bayesian Risk Markov Decision Processes

Yifan Lin, Enlu Zhou

Combining Priors with Experience: Confidence Calibration Based on Binomial Process Modeling

Jinzong Dong, Zhaohui Jiang, Dong Pan et al.

REPEAT: Improving Uncertainty Estimation in Representation Learning Explainability

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

Quantifying Uncertainty in the Presence of Distribution Shifts

Yuli Slavutsky, David Blei

Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception

Luke Chen, Junyao Wang, Trier Mortlock et al.

Regret Bounds for Episodic Risk-Sensitive Linear Quadratic Regulator

Wenhao Xu, Xuefeng Gao, Xuedong He

Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory

Dominik Fuchsgruber, Tom Wollschläger, Johannes Bordne et al.

Inverse decision-making using neural amortized Bayesian actors

Dominik Straub, Tobias Fabian Niehues, Jan Peters et al.

Statistical Inference for Gradient Boosting Regression

Haimo Fang, Kevin Tan, Giles Hooker

Optimal kernel regression bounds under energy-bounded noise

Amon Lahr, Johannes Köhler, Anna Scampicchio et al.

Adjusted Count Quantification Learning on Graphs

Clemens Damke, Eyke Hüllermeier

AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking

Soyoung Yoon, Gyuwan Kim, Gyu-Hwung Cho et al.

Leveraging Uncertainty Estimates To Improve Classifier Performance

Gundeep Arora, Srujana Merugu, Anoop Saladi et al.

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

Qi Chen, Hu Ding

Active Measurement: Efficient Estimation at Scale

Max Hamilton, Jinlin Lai, Wenlong Zhao et al.

Self-Calibrating Vicinal Risk Minimisation for Model Calibration

Jiawei Liu, Changkun Ye, Ruikai Cui et al.

Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture

Juanwu Lu, Can Cui, Yunsheng Ma et al.

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.

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

Frank Shih, Zhenghao Jiang, Faming Liang

Uncertainty-Aware Yield Prediction with Multimodal Molecular Features

Jiayuan Chen, Kehan Guo, Zhen Liu et al.

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

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

Ji Won Park, Kyunghyun Cho

Torch-Uncertainty: Deep Learning Uncertainty Quantification

Adrien Lafage, Olivier Laurent, Firas Gabetni et al.

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

Tailin Wu, Willie Neiswanger, Hongtao Zheng et al.

Towards Reliable LLM-based Robots Planning via Combined Uncertainty Estimation

Shiyuan Yin, Chenjia Bai, Zihao Zhang et al.

Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction

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

Partially Matching Submap Helps: Uncetainty Modeling and Propagation for Text to Point Cloud Localization

Mingtao Feng, Longlong Mei, Zijie Wu et al.

Probabilistic Explanations for Linear Models

Bernardo Subercaseaux, Marcelo Arenas, Kuldeep S. Meel