Bayesian & Probabilistic

Smooth Shells: Multi-Scale Shape Registration With Functional Maps CVPR 2020 Solving General Elliptical Mixture Models through an Approximate Wasserstein Manifold AAAI 2020 Solving Multiple Inference by Minimizing Expected Loss PGM 2020 Source Separation with Deep Generative Priors ICML 2020 Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models L4DC 2020 Sparse Orthogonal Variational Inference for Gaussian Processes AISTATS 2020 Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning NeurIPS 2020 Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes AAAI 2020 Spike and slab variational Bayes for high dimensional logistic regression NeurIPS 2020 Spread Divergence ICML 2020 State Variable Effects in Graphical Event Models IJCAI 2020 Stein Self-Repulsive Dynamics: Benefits From Past Samples NeurIPS 2020 Stochastic Convolutional Recurrent Networks for Language Modeling Interspeech 2020 Stochastic Deep Gaussian Processes over Graphs NeurIPS 2020 Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020 Stochastic Stein Discrepancies NeurIPS 2020 Structural Causal Models Are (Solvable by) Credal Networks PGM 2020 Structured Output Learning with Conditional Generative Flows AAAI 2020 Structured Prediction for Joint Class Cardinality and Entity Property Inference in Model-Complete Text Comprehension EMNLP 2020 Structured Probabilistic End-to-End Learning from Crowds IJCAI 2020 Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models L4DC 2020 Structure Learning from Related Data Sets with a Hierarchical Bayesian Score PGM 2020 Strudel: Learning Structured-Decomposable Probabilistic Circuits PGM 2020 Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces NeurIPS 2020 Sum-Product Network Decompilation PGM 2020