uncertainty quantification

AutoIP: A United Framework to Integrate Physics into Gaussian Processes ICML 2022 A Variational Bayesian Method for Similarity Learning in Non-Rigid Image Registration CVPR 2022 Azimuth: Systematic Error Analysis for Text Classification EMNLP 2022 Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How ICLR 2022 Bayesian spillover graphs for dynamic networks UAI 2022 Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty NeurIPS 2022 Bayesian subset selection and variable importance for interpretable prediction and classification JMLR 2022 Being a Bit Frequentist Improves Bayesian Neural Networks AISTATS 2022 Calibrated and Sharp Uncertainties in Deep Learning via Density Estimation ICML 2022 Calibration of Machine Reading Systems at Scale ACL 2022 CARD: Classification and Regression Diffusion Models NeurIPS 2022 Causal discovery with heterogeneous observational data UAI 2022 Centroid Approximation for Bootstrap: Improving Particle Quality at Inference ICML 2022 Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss AAAI 2022 Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learning AAAI 2022 Contour-Hugging Heatmaps for Landmark Detection CVPR 2022 Counterfactual Plans under Distributional Ambiguity ICLR 2022 Data-Driven Conditional Robust Optimization NeurIPS 2022 Deep Ensembles Work, But Are They Necessary? NeurIPS 2022 Deep Hybrid Models for Out-of-Distribution Detection CVPR 2022 Deep Probability Estimation ICML 2022 Designing a Human-in-the-Loop System for Object Detection in Floor Plans AAAI 2022 DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator ICLR 2022 Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model AAAI 2022 Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel NeurIPS 2022