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Uncertainty Quantification

663 directly classified papers

Papers per year

Papers

PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction NIPS 2023
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability NIPS 2023
Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation NIPS 2023
When to Read Documents or QA History: On Unified and Selective Open-domain QA ACL 2023
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study EMNLP 2023
A Critical Analysis of Document Out-of-Distribution Detection EMNLP 2023
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs EMNLP 2023
Conformalization of Sparse Generalized Linear Models ICML 2023
Conformal Prediction Sets for Graph Neural Networks ICML 2023
Transfer Knowledge From Head to Tail: Uncertainty Calibration Under Long-Tailed Distribution CVPR 2023
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations NIPS 2023
Beyond Confidence: Reliable Models Should Also Consider Atypicality NIPS 2023
CoDrug: Conformal Drug Property Prediction with Density Estimation under Covariate Shift NIPS 2023
Uncertainty Quantification via Neural Posterior Principal Components NIPS 2023
Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization NIPS 2023
Why Did This Model Forecast This Future? Information-Theoretic Saliency for Counterfactual Explanations of Probabilistic Regression Models NIPS 2023
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification NIPS 2023
Confidence and Uncertainty Assessment for Distributional Random Forests JMLR 2023
Selection by Prediction with Conformal p-values JMLR 2023
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation JMLR 2023
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes ICML 2023
R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents ICML 2023
PAC Prediction Sets for Large Language Models of Code ICML 2023
Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing NIPS 2023
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift NIPS 2023