Bayesian Learning

Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling JMLR 2016 Incremental Variational Sparse Gaussian Process Regression NeurIPS 2016 Interactive Bayesian Hierarchical Clustering ICML 2016 K2-ABC: Approximate Bayesian Computation with Kernel Embeddings AISTATS 2016 Kernel Bayesian Inference with Posterior Regularization NeurIPS 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 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 Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models NeurIPS 2016 Natural-Parameter Networks: A Class of Probabilistic Neural Networks NeurIPS 2016 Nonparametric Network Models for Link Prediction JMLR 2016 Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo AISTATS 2016 Observational-Interventional Priors for Dose-Response Learning NeurIPS 2016 Online Algorithms for Sum-Product Networks with Continuous Variables PGM 2016 Online Learning With Bayesian Classification Trees CVPR 2016 On Pruning with the MDL Score PGM 2016 Poisson-Gamma dynamical systems NeurIPS 2016 Provable Bayesian Inference via Particle Mirror Descent AISTATS 2016 Regime Aware Learning PGM 2016 Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits COLT 2016 Sampling for Bayesian Program Learning NeurIPS 2016 Scalable Structure Discovery in Regression using Gaussian Processes AutoML 2016