Markov Chain Monte Carlo

Scaling-up Importance Sampling for Markov Logic Networks NeurIPS 2014 Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models NeurIPS 2014 Sequential Monte Carlo for Graphical Models NeurIPS 2014 The Infinite Mixture of Infinite Gaussian Mixtures NeurIPS 2014 Analyzing Hogwild Parallel Gaussian Gibbs Sampling NeurIPS 2013 Annealing between distributions by averaging moments NeurIPS 2013 Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions NeurIPS 2013 Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models NeurIPS 2013 Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC NeurIPS 2013 Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent NeurIPS 2013 Computing the Stationary Distribution Locally NeurIPS 2013 Fast Determinantal Point Process Sampling with Application to Clustering NeurIPS 2013 Flexible sampling of discrete data correlations without the marginal distributions NeurIPS 2013 Generalized Denoising Auto-Encoders as Generative Models NeurIPS 2013 Generalized Random Utility Models with Multiple Types NeurIPS 2013 Learning Stochastic Inverses NeurIPS 2013 Locally Adaptive Bayesian Multivariate Time Series NeurIPS 2013 Minimum Uncertainty Gap for Robust Visual Tracking CVPR 2013 On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations NeurIPS 2013 Parallel Sampling of DP Mixture Models using Sub-Cluster Splits NeurIPS 2013 Probabilistic Principal Geodesic Analysis NeurIPS 2013 Projecting Ising Model Parameters for Fast Mixing NeurIPS 2013 Scalable Inference for Logistic-Normal Topic Models NeurIPS 2013 Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex NeurIPS 2013 A Marginalized Particle Gaussian Process Regression NeurIPS 2012