Bayesian & Probabilistic

K2-ABC: Approximate Bayesian Computation with Kernel Embeddings AISTATS 2016 Kernel Bayesian Inference with Posterior Regularization NeurIPS 2016 Kronecker Determinantal Point Processes NeurIPS 2016 Latent Point Process Allocation AISTATS 2016 Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP) CVPR 2016 Learning Complex Uncertain States Changes via Asymmetric Hidden Markov Models: an Industrial Case PGM 2016 Learning Parameters of Hybrid Time Bayesian Networks PGM 2016 Learning Planar Ising Models JMLR 2016 Learning Tractable Multidimensional Bayesian Network Classifiers PGM 2016 Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables NeurIPS 2016 Learning under uncertainty: a comparison between R-W and Bayesian approach NeurIPS 2016 Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification CVPR 2016 Making Large Cox’s Proportional Hazard Models Tractable in Bayesian Networks PGM 2016 Markov-modulated Marked Poisson Processes for Check-in Data ICML 2016 Measuring the reliability of MCMC inference with bidirectional Monte Carlo NeurIPS 2016 Minimax Adaptive Estimation of Nonparametric Hidden Markov Models JMLR 2016 Mixed vine copulas as joint models of spike counts and local field potentials NeurIPS 2016 Mixture of Bilateral-Projection Two-Dimensional Probabilistic Principal Component Analysis CVPR 2016 Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes COLT 2016 Multi-Label Classification with Cutset Networks PGM 2016 Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models NeurIPS 2016 Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula NeurIPS 2016 Natural-Parameter Networks: A Class of Probabilistic Neural Networks NeurIPS 2016 Near-Optimal Smoothing of Structured Conditional Probability Matrices NeurIPS 2016 Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks NeurIPS 2016