mcmc sampling

Flexible Modeling of Diversity with Strongly Log-Concave Distributions NeurIPS 2019 Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019 Shared Subspace Models for Multi-Group Covariance Estimation JMLR 2019 Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments NeurIPS 2018 Exponentiated Strongly Rayleigh Distributions NeurIPS 2018 Fast Estimation of Causal Interactions using Wold Processes NeurIPS 2018 Learning Descriptor Networks for 3D Shape Synthesis and Analysis CVPR 2018 Learning Generative ConvNets via Multi-Grid Modeling and Sampling CVPR 2018 Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC ICML 2017 Zonotope Hit-and-run for Efficient Sampling from Projection DPPs ICML 2017 Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling NeurIPS 2016 Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling JMLR 2016 Stochastic Gradient MCMC with Stale Gradients NeurIPS 2016 Learning Fast-Mixing Models for Structured Prediction ICML 2015 MCMC Learning COLT 2015 Reflection, Refraction, and Hamiltonian Monte Carlo NeurIPS 2015 Approximate Slice Sampling for Bayesian Posterior Inference AISTATS 2014 Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models NeurIPS 2014 Parallel Sampling of HDPs using Sub-Cluster Splits NeurIPS 2014 PEWA: Patch-based Exponentially Weighted Aggregation for image denoising NeurIPS 2014 Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data NeurIPS 2014 Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models NeurIPS 2014 Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus AISTATS 2014 Adaptive Hamiltonian and Riemann Manifold Monte Carlo ICML 2013 Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions NeurIPS 2013