Markov Chain Monte Carlo

Stochastic Gradient Monomial Gamma Sampler ICML 2017 Completely random measures for modelling block-structured sparse networks NeurIPS 2016 Coresets for Scalable Bayesian Logistic Regression NeurIPS 2016 Learning Parameters of Hybrid Time Bayesian Networks PGM 2016 Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification CVPR 2016 Measuring the reliability of MCMC inference with bidirectional Monte Carlo NeurIPS 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 Synthesis of MCMC and Belief Propagation NeurIPS 2016 Towards Unifying Hamiltonian Monte Carlo and Slice Sampling NeurIPS 2016 A Complete Recipe for Stochastic Gradient MCMC NeurIPS 2015 A hybrid sampler for Poisson-Kingman mixture models NeurIPS 2015 Bayesian dark knowledge NeurIPS 2015 Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling NeurIPS 2015 Exponential Integration for Hamiltonian Monte Carlo ICML 2015 Fast Bidirectional Probability Estimation in Markov Models NeurIPS 2015 Finite-Time Analysis of Projected Langevin Monte Carlo NeurIPS 2015 Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families NeurIPS 2015 Inference for determinantal point processes without spectral knowledge NeurIPS 2015 Infinite Factorial Dynamical Model NeurIPS 2015 Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets NeurIPS 2015 MCMC for Variationally Sparse Gaussian Processes NeurIPS 2015 Measuring Sample Quality with Stein's Method NeurIPS 2015