Bayesian & Probabilistic

Riemannian Laplace approximations for Bayesian neural networks NeurIPS 2023 Robust and Adaptive Deep Learning via Bayesian Principles AAAI 2023 Robust and Scalable Bayesian Online Changepoint Detection ICML 2023 Robust and Scalable Gaussian Process Regression and Its Applications CVPR 2023 Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees ICML 2023 Robust Outlier Rejection for 3D Registration With Variational Bayes CVPR 2023 RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data ICLR 2023 Safety Verification of Nonlinear Systems with Bayesian Neural Network Controllers AAAI 2023 SAM as an Optimal Relaxation of Bayes ICLR 2023 Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent NeurIPS 2023 Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing ICLR 2023 Scalable Bayesian Meta-Learning through Generalized Implicit Gradients AAAI 2023 Scalable Deep Kernel Gaussian Process for Vehicle Dynamics in Autonomous Racing CoRL 2023 Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance JMLR 2023 Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel ICLR 2023 Scaling integer arithmetic in probabilistic programs UAI 2023 Scaling Up Probabilistic Circuits by Latent Variable Distillation ICLR 2023 Score-based Data Assimilation NeurIPS 2023 Score-Based Learning of Graphical Event Models with Background Knowledge Augmentation AAAI 2023 Self-Correcting Bayesian Optimization through Bayesian Active Learning NeurIPS 2023 Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations ICML 2023 Semi-Implicit Variational Inference via Score Matching ICLR 2023 Sentiment as an Ordinal Latent Variable EACL 2023 Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference AAAI 2023 Sequence Learning Using Equilibrium Propagation IJCAI 2023