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epistemic uncertainty

156 papers

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EU

Co-occurring keywords

uncertainty quantification (1365) aleatoric uncertainty (105) out-of-distribution detection (611) uncertainty estimation (639) bayesian neural network (201) neural network (6616) bayesian inference (1904) reinforcement learning (4122) active learning (1295) large language model (12755)

Papers

Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes AISTATS 2021
Risk-Averse Zero-Order Trajectory Optimization CORL 2021
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation WACV 2021
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties AISTATS 2021
Active Learning for Deep Object Detection via Probabilistic Modeling ICCV 2021
Meta-Learning Reliable Priors in the Function Space NIPS 2021
On Hallucination and Predictive Uncertainty in Conditional Language Generation EACL 2021
Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization CVPR 2021
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning ICML 2021
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability NIPS 2021
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models NIPS 2021
Sketching curvature for efficient out-of-distribution detection for deep neural networks UAI 2021
Perception Beyond Sensors Under Uncertainty AAAI 2021
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions NIPS 2021
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts NIPS 2020
Variational Learning of Bayesian Neural Networks via Bayesian Dark Knowledge IJCAI 2020
Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents AAAI 2020
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates ICML 2020
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? ICML 2020
Justification-Based Reliability in Machine Learning AAAI 2020
Uncertainty-Aware Deep Classifiers Using Generative Models AAAI 2020
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles UAI 2020
Automatic Curriculum Learning through Value Disagreement NIPS 2020
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning NIPS 2020
Deep Evidential Regression NIPS 2020