Bayesian & Probabilistic

Identification and Overidentification of Linear Structural Equation Models NeurIPS 2016 Identifying the irreducible disjoint factors of a multivariate probability distribution PGM 2016 Improving Distributed Word Representation and Topic Model by Word-Topic Mixture Model ACML 2016 Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling JMLR 2016 Incremental Variational Sparse Gaussian Process Regression NeurIPS 2016 Inference Networks for Sequential Monte Carlo in Graphical Models ICML 2016 Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs COLING 2016 Interacting Particle Markov Chain Monte Carlo ICML 2016 Isotonic Hawkes Processes ICML 2016 Iterative Refinement of the Approximate Posterior for Directed Belief Networks NeurIPS 2016 Joint Bayesian Modelling of Internal Dependencies and Relevant Multimorbidities of a Heterogeneous Disease PGM 2016 Joint Probabilistic Matching Using m-Best Solutions CVPR 2016 Joint Structural Estimation of Multiple Graphical Models JMLR 2016 Kernel Mean Shrinkage Estimators JMLR 2016 Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes NeurIPS 2016 Kronecker Determinantal Point Processes NeurIPS 2016 Learning and Inference via Maximum Inner Product Search ICML 2016 Learning Bayesian networks with ancestral constraints NeurIPS 2016 Learning Complex Uncertain States Changes via Asymmetric Hidden Markov Models: an Industrial Case PGM 2016 Learning Granger Causality for Hawkes Processes ICML 2016 Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices NeurIPS 2016 Learning Mixtures of Plackett-Luce Models ICML 2016 Learning Parameters of Hybrid Time Bayesian Networks PGM 2016 Learning Planar Ising Models JMLR 2016 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016