Bayesian & Probabilistic

An Algorithm to Learn Polytree Networks with Hidden Nodes NeurIPS 2019 A neurally plausible model for online recognition and postdiction in a dynamical environment NeurIPS 2019 Anytime Recursive Best-First Search for Bounding Marginal MAP AAAI 2019 A Particle-Based Variational Approach to Bayesian Non-negative Matrix Factorization JMLR 2019 Approximate Causal Abstractions UAI 2019 Approximate Inference in Structured Instances with Noisy Categorical Observations UAI 2019 A Probabilistic Derivation of LASSO and L12-Norm Feature Selections AAAI 2019 Augmenting and Tuning Knowledge Graph Embeddings UAI 2019 Automated Scalable Bayesian Inference via Hilbert Coresets JMLR 2019 A Weighted Mini-Bucket Bound for Solving Influence Diagram UAI 2019 Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era AISTATS 2019 Band-Limited Gaussian Processes: The Sinc Kernel NeurIPS 2019 Bayesian Combination of Probabilistic Classifiers using Multivariate Normal Mixtures JMLR 2019 Bayesian Execution Skill Estimation AAAI 2019 Bayesian Functional Optimisation with Shape Prior AAAI 2019 Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification AAAI 2019 Bayesian Hierarchical Dynamic Model for Human Action Recognition CVPR 2019 Bayesian Layers: A Module for Neural Network Uncertainty NeurIPS 2019 Bayesian Learning of Neural Network Architectures AISTATS 2019 Bayesian Loss for Crowd Count Estimation With Point Supervision ICCV 2019 Bayesian Optimization for Policy Search via Online-Offline Experimentation JMLR 2019 Bayesian Optimization Meets Riemannian Manifolds in Robot Learning CoRL 2019 Bayesian Optimization under Heavy-tailed Payoffs NeurIPS 2019 Bayesian Optimization with Binary Auxiliary Information UAI 2019 Bayesian Optimization with Unknown Search Space NeurIPS 2019