Markov Chain Monte Carlo

Efficient Learning of Restricted Boltzmann Machines Using Covariance Estimates ACML 2019 Estimating Convergence of Markov chains with L-Lag Couplings NeurIPS 2019 Flexible Modeling of Diversity with Strongly Log-Concave Distributions NeurIPS 2019 Gradient-based Adaptive Markov Chain Monte Carlo NeurIPS 2019 Nonconvex sampling with the Metropolis-adjusted Langevin algorithm COLT 2019 Online sampling from log-concave distributions NeurIPS 2019 Parameter elimination in particle Gibbs sampling NeurIPS 2019 Probabilistic Permutation Synchronization Using the Riemannian Structure of the Birkhoff Polytope CVPR 2019 Pseudo-Extended Markov chain Monte Carlo NeurIPS 2019 Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices NeurIPS 2019 Self-Adversarially Learned Bayesian Sampling AAAI 2019 SpHMC: Spectral Hamiltonian Monte Carlo AAAI 2019 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction NeurIPS 2019 Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NeurIPS 2019 The Randomized Midpoint Method for Log-Concave Sampling NeurIPS 2019 Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling ICLR 2019 A Faster Sampling Algorithm for Spherical k-means ACML 2018 Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo NeurIPS 2018 HOGWILD!-Gibbs can be PanAccurate NeurIPS 2018 Large-Scale Stochastic Sampling from the Probability Simplex NeurIPS 2018 Mental Sampling in Multimodal Representations NeurIPS 2018 The promises and pitfalls of Stochastic Gradient Langevin Dynamics NeurIPS 2018 A-NICE-MC: Adversarial Training for MCMC NeurIPS 2017 Collapsed variational Bayes for Markov jump processes NeurIPS 2017 Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks NeurIPS 2017