uncertainty quantification

Empirical Priors for Prediction in Sparse High-dimensional Linear Regression JMLR 2020 Enriched mixtures of generalised Gaussian process experts AISTATS 2020 Ensemble Approaches for Uncertainty in Spoken Language Assessment Interspeech 2020 Ensemble of Students Taught by Probabilistic Teachers to Improve Speech Emotion Recognition Interspeech 2020 Ensembling geophysical models with Bayesian Neural Networks NeurIPS 2020 Estimating predictive uncertainty for rumour verification models ACL 2020 Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation NeurIPS 2020 Fighting Wildfires under Uncertainty - A Sequential Resource Allocation Approach IJCAI 2020 Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions ICML 2020 Gaze Estimation for Assisted Living Environments WACV 2020 Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks UAI 2020 Gradient-EM Bayesian Meta-Learning NeurIPS 2020 Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks IJCAI 2020 Healing Products of Gaussian Process Experts ICML 2020 Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights NeurIPS 2020 How Good is the Bayes Posterior in Deep Neural Networks Really? ICML 2020 Hyperparameter Ensembles for Robustness and Uncertainty Quantification NeurIPS 2020 Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models NeurIPS 2020 Improving model calibration with accuracy versus uncertainty optimization NeurIPS 2020 Incorporating Interpretable Output Constraints in Bayesian Neural Networks NeurIPS 2020 Influenza Forecasting Framework based on Gaussian Processes ICML 2020 Interpretable Deep Gaussian Processes with Moments AISTATS 2020 Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds ACL 2020 Justification-Based Reliability in Machine Learning AAAI 2020 Knowing-How under Uncertainty (Extended Abstract) IJCAI 2020