Probabilistic Modeling

A Probabilistic Model of Social Decision Making based on Reward Maximization NeurIPS 2016 A Probabilistic Programming Approach To Probabilistic Data Analysis NeurIPS 2016 A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification NeurIPS 2016 Attend, Infer, Repeat: Fast Scene Understanding with Generative Models NeurIPS 2016 A Unified Approach for Learning the Parameters of Sum-Product Networks NeurIPS 2016 Bayesian Graphical Models for Multivariate Functional Data JMLR 2016 Bayesian group factor analysis with structured sparsity JMLR 2016 Bayesian Optimization for Probabilistic Programs NeurIPS 2016 Bayesian Optimization with Robust Bayesian Neural Networks NeurIPS 2016 Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations ICML 2016 Boolean Matrix Factorization and Noisy Completion via Message Passing ICML 2016 Bottom-Up and Top-Down Reasoning With Hierarchical Rectified Gaussians CVPR 2016 Clinical Tagging with Joint Probabilistic Models MLHC 2016 Collapsed Variational Inference for Sum-Product Networks ICML 2016 Composing graphical models with neural networks for structured representations and fast inference NeurIPS 2016 Computationally Efficient Bayesian Learning of Gaussian Process State Space Models AISTATS 2016 Conditional Bernoulli Mixtures for Multi-label Classification ICML 2016 Conditional Probability Estimation PGM 2016 Consistent Kernel Mean Estimation for Functions of Random Variables NeurIPS 2016 Constraints Based Convex Belief Propagation NeurIPS 2016 Cooperative Graphical Models NeurIPS 2016 Copula Ordinal Regression for Joint Estimation of Facial Action Unit Intensity CVPR 2016 Data Programming: Creating Large Training Sets, Quickly NeurIPS 2016 Decisions and Dependence in Influence Diagrams PGM 2016 DISCO Nets : DISsimilarity COefficients Networks NeurIPS 2016