Bayesian & Probabilistic

Sequential Neural Processes NeurIPS 2019 Shared Subspace Models for Multi-Group Covariance Estimation JMLR 2019 ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness CVPR 2019 Single-Model Uncertainties for Deep Learning NeurIPS 2019 Sketching for Latent Dirichlet-Categorical Models AISTATS 2019 Smoothing Structured Decomposable Circuits NeurIPS 2019 Smoothing the Geometry of Probabilistic Box Embeddings ICLR 2019 Sparse Variational Inference: Bayesian Coresets from Scratch NeurIPS 2019 Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs NeurIPS 2019 Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model AAAI 2019 SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity AISTATS 2019 Stein Point Markov Chain Monte Carlo ICML 2019 Stein Variational Gradient Descent With Matrix-Valued Kernels NeurIPS 2019 Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels ICCV 2019 Stochastic Optimal Control as Approximate Input Inference CoRL 2019 Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NeurIPS 2019 Stratified Evidence Logics IJCAI 2019 Streaming Bayesian Inference for Crowdsourced Classification NeurIPS 2019 Structured Bayesian Networks: From Inference to Learning with Routes AAAI 2019 Structured Variational Inference in Continuous Cox Process Models NeurIPS 2019 Sum-of-Squares Polynomial Flow ICML 2019 Switching Linear Dynamics for Variational Bayes Filtering ICML 2019 Temporal Difference Variational Auto-Encoder ICLR 2019 Tensorial Change Analysis Using Probabilistic Tensor Regression AAAI 2019 Tensor Monte Carlo: Particle Methods for the GPU era NeurIPS 2019