markov chain monte carlo

Scaling up Data Augmentation MCMC via Calibration JMLR 2018 Simple, Distributed, and Accelerated Probabilistic Programming NeurIPS 2018 Stein Variational Gradient Descent Without Gradient ICML 2018 Stochastic Fractional Hamiltonian Monte Carlo IJCAI 2018 Stochastic Variance-Reduced Hamilton Monte Carlo Methods ICML 2018 Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series AISTATS 2018 The promises and pitfalls of Stochastic Gradient Langevin Dynamics NeurIPS 2018 Underdamped Langevin MCMC: A non-asymptotic analysis COLT 2018 A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI ICML 2017 AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms NeurIPS 2017 A-NICE-MC: Adversarial Training for MCMC NeurIPS 2017 Annular Augmentation Sampling AISTATS 2017 Asymptotically exact inference in differentiable generative models AISTATS 2017 A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing IJCAI 2017 Bayesian Tensor Regression JMLR 2017 Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling NeurIPS 2017 Collapsed variational Bayes for Markov jump processes NeurIPS 2017 CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC AISTATS 2017 Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling IJCAI 2017 Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo ICML 2017 Further and stronger analogy between sampling and optimization: Langevin Monte Carlo and gradient descent COLT 2017 Generalized Pólya Urn for Time-Varying Pitman-Yor Processes JMLR 2017 Hit-and-Run for Sampling and Planning in Non-Convex Spaces AISTATS 2017 Improving Gibbs Sampler Scan Quality with DoGS ICML 2017 Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo ICML 2017