Bayesian & Probabilistic

Scalable MAP inference in Bayesian networks based on a Map-Reduce approach PGM 2016 Scalable MCMC for Mixed Membership Stochastic Blockmodels AISTATS 2016 Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages NeurIPS 2016 Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings COLING 2016 Simulation and Calibration of a Fully Bayesian Marked Multidimensional Hawkes Process with Dissimilar Decays ACML 2016 Spectral Learning of Dynamic Systems from Nonequilibrium Data NeurIPS 2016 Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing JMLR 2016 Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies ICML 2016 Stable Graphical Models JMLR 2016 Statistical Inference for Pairwise Graphical Models Using Score Matching NeurIPS 2016 Statistical Matching of Discrete Data by Bayesian Networks PGM 2016 Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm NeurIPS 2016 Stochastic Discrete Clenshaw-Curtis Quadrature ICML 2016 Stochastic Gradient Geodesic MCMC Methods NeurIPS 2016 Stochastic Gradient MCMC with Stale Gradients NeurIPS 2016 Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo NeurIPS 2016 Stochastic Quasi-Newton Langevin Monte Carlo ICML 2016 Stochastic Variational Inference for the HDP-HMM AISTATS 2016 Structured Prediction Theory Based on Factor Graph Complexity NeurIPS 2016 Structure Learning of Partitioned Markov Networks ICML 2016 Student Skill Models in Adaptive Testing PGM 2016 Supervised Learning of Acoustic Models in a Zero Resource Setting to Improve DPGMM Clustering Interspeech 2016 Synthesis of MCMC and Belief Propagation NeurIPS 2016 The Effect of Combination Functions on the Complexity of Relational Bayesian Networks PGM 2016 The Multiple Quantile Graphical Model NeurIPS 2016