mixing time

Mixing Time Estimation in Ergodic Markov Chains from a Single Trajectory with Contraction Methods ALT 2020 Unknown mixing times in apprenticeship and reinforcement learning UAI 2020 Estimating the Mixing Time of Ergodic Markov Chains COLT 2019 Large Scale Markov Decision Processes with Changing Rewards NeurIPS 2019 Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019 Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions 2: Numerical integrators AISTATS 2019 Random Walks on Hypergraphs with Edge-Dependent Vertex Weights ICML 2019 Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo NeurIPS 2018 Exponentiated Strongly Rayleigh Distributions NeurIPS 2018 Fast MCMC Sampling Algorithms on Polytopes JMLR 2018 Layerwise Systematic Scan: Deep Boltzmann Machines and Beyond AISTATS 2018 Log-concave sampling: Metropolis-Hastings algorithms are fast! COLT 2018 Efficient Sampling from Time-Varying Log-Concave Distributions JMLR 2017 Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling IJCAI 2017 Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models AISTATS 2017 The Mixing of Markov Chains on Linear Extensions in Practice IJCAI 2017 Zonotope Hit-and-run for Efficient Sampling from Projection DPPs ICML 2017 Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling ICML 2016 Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling NeurIPS 2016 Monte Carlo Markov Chain Algorithms for Sampling Strongly Rayleigh Distributions and Determinantal Point Processes COLT 2016 Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much NeurIPS 2016 Learning Fast-Mixing Models for Structured Prediction ICML 2015 Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets NeurIPS 2015 Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path NeurIPS 2015 Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width NeurIPS 2015