epistemic uncertainty

Uncertainty in Causality: A New Frontier ACL 2025 Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations ICLR 2025 Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models? ACL 2025 UOREX: Towards Uncertainty-Aware Open Relation Extraction NAACL 2025 Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks ICLR 2024 Active preference learning for ordering items in- and out-of-sample NeurIPS 2024 Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? NeurIPS 2024 Bayesian Exploration Networks ICML 2024 Conformalized Credal Set Predictors NeurIPS 2024 Credal Deep Ensembles for Uncertainty Quantification NeurIPS 2024 Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling ICML 2024 Deep Bayesian Active Learning for Preference Modeling in Large Language Models NeurIPS 2024 Demystifying Algorithmic Fairness in an Uncertain World AAAI 2024 Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression AAAI 2024 Distinguishing the Knowable from the Unknowable with Language Models ICML 2024 Dynamic Knowledge Injection for AIXI Agents AAAI 2024 Energy-based Epistemic Uncertainty for Graph Neural Networks NeurIPS 2024 Epistemic Uncertainty Quantification For Pre-Trained Neural Networks CVPR 2024 Estimating Epistemic and Aleatoric Uncertainty with a Single Model NeurIPS 2024 Evidential Uncertainty Quantification: A Variance-Based Perspective WACV 2024 Experts Don’t Cheat: Learning What You Don’t Know By Predicting Pairs ICML 2024 Fixing Overconfidence in Dynamic Neural Networks WACV 2024 FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning NeurIPS 2024 Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting IJCAI 2024 Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification ICML 2024