Bayesian & Probabilistic

Scalable Approximate MCMC Algorithms for the Horseshoe Prior JMLR 2020 Scalable Belief Propagation via Relaxed Scheduling NeurIPS 2020 Scalable Exact Inference in Multi-Output Gaussian Processes ICML 2020 Scalable Gaussian Process Regression Networks IJCAI 2020 Scalable Gaussian Process Separation for Kernels with a Non-Stationary Phase ICML 2020 Scalable Inference on the Soft Affiliation Graph Model for Overlapping Community Detection ACML 2020 Scalable Nonparametric Factorization for High-Order Interaction Events AISTATS 2020 Scalable Probabilistic Matrix Factorization with Graph-Based Priors AAAI 2020 Scalable Uncertainty for Computer Vision With Functional Variational Inference CVPR 2020 Scaling Up AND/OR Abstraction Sampling IJCAI 2020 Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing ICML 2020 SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates ICML 2020 Second Order PAC-Bayesian Bounds for the Weighted Majority Vote NeurIPS 2020 Self-Modulating Nonparametric Event-Tensor Factorization ICML 2020 Semi-Modular Inference: enhanced learning in multi-modular models by tempering the influence of components AISTATS 2020 Semi-parametric Learning of Structured Temporal Point Processes JMLR 2020 Semi-supervised Parsing with a Variational Autoencoding Parser ACL 2020 SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback AAAI 2020 Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness NeurIPS 2020 Slice Sampling for General Completely Random Measures UAI 2020 Solving Multiple Inference by Minimizing Expected Loss PGM 2020 Sparse Gaussian Processes with Spherical Harmonic Features ICML 2020 Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes AAAI 2020 Spectral Subsampling MCMC for Stationary Time Series ICML 2020 Spike and slab variational Bayes for high dimensional logistic regression NeurIPS 2020