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metropolis-hastings algorithm

25 papers

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markov chain monte carlo (725) bayesian inference (1907) mixing time (70) large-scale inference (4) accept-reject step (2) markov chain (207) mcmc sampling (70) posterior sampling (152) data augmentation (3052) approximate inference (253)

Papers

Length Controlled Generation for Black-box LLMs ACL 2025
Graph-accelerated Markov Chain Monte Carlo using Approximate Samples JMLR 2025
Piecewise deterministic sampling with splitting schemes JMLR 2025
On the Computational Complexity of Private High-dimensional Model Selection NIPS 2024
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC JMLR 2024
Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation AISTATS 2023
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference ICML 2023
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference NIPS 2022
Orbital MCMC AISTATS 2022
An Efficient Sampling Algorithm for Non-smooth Composite Potentials JMLR 2022
Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems NIPS 2022
Metropolis-Hastings Data Augmentation for Graph Neural Networks NIPS 2021
Mixing Time of Metropolis-Hastings for Bayesian Community Detection JMLR 2021
Maximal Couplings of the Metropolis-Hastings Algorithm AISTATS 2021
Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019
The Implicit Metropolis-Hastings Algorithm NIPS 2019
Scaling up Data Augmentation MCMC via Calibration JMLR 2018
On Markov chain Monte Carlo methods for tall data JMLR 2017
Learning Co-Substructures by Kernel Dependence Maximization IJCAI 2017
Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling JMLR 2016
Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM AISTATS 2015
Kernel Adaptive Metropolis-Hastings ICML 2014
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach ICML 2014
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models NIPS 2013
Burn-in, bias, and the rationality of anchoring NIPS 2012