Markov Chain Monte Carlo

Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers NeurIPS 2022 Constrained Langevin Algorithms with L-mixing External Random Variables NeurIPS 2022 Continuously Tempered PDMP samplers NeurIPS 2022 Dimension-free convergence rates for gradient Langevin dynamics in RKHS COLT 2022 Distribution Compression in Near-Linear Time ICLR 2022 Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting JMLR 2022 Efficient Sampling on Riemannian Manifolds via Langevin MCMC NeurIPS 2022 Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings ICLR 2022 FedPop: A Bayesian Approach for Personalised Federated Learning NeurIPS 2022 Fixed-Distance Hamiltonian Monte Carlo NeurIPS 2022 Generalized Kernel Thinning ICLR 2022 Improved analysis for a proximal algorithm for sampling COLT 2022 Interacting Contour Stochastic Gradient Langevin Dynamics ICLR 2022 Langevin Autoencoders for Learning Deep Latent Variable Models NeurIPS 2022 Local-Global MCMC kernels: the best of both worlds NeurIPS 2022 Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients NeurIPS 2022 Multi-fidelity Monte Carlo: a pseudo-marginal approach NeurIPS 2022 Nonlinear MCMC for Bayesian Machine Learning NeurIPS 2022 Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network NeurIPS 2022 Path Auxiliary Proposal for MCMC in Discrete Space ICLR 2022 Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems NeurIPS 2022 Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods COLT 2022 Sampling with Mirrored Stein Operators ICLR 2022 Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space NeurIPS 2022 Sliced Wasserstein variational inference ACML 2022