metropolis-hastings algorithm

Making the Classification Explanation Faithful to the Confidence Score CVPR 2026 Bayesian Decision Theory on Decision Trees: Uncertainty Evaluation and Interpretability AISTATS 2025 Graph-accelerated Markov Chain Monte Carlo using Approximate Samples JMLR 2025 Length Controlled Generation for Black-box LLMs ACL 2025 Non-Equilibrium Dynamics of Hybrid Continuous-Discrete Ground-State Sampling ICLR 2025 Piecewise deterministic sampling with splitting schemes JMLR 2025 Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC JMLR 2024 On the Computational Complexity of Private High-dimensional Model Selection NeurIPS 2024 Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy ICLR 2024 DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference ICML 2023 Efficient Informed Proposals for Discrete Distributions via Newton’s Series Approximation AISTATS 2023 An Efficient Sampling Algorithm for Non-smooth Composite Potentials JMLR 2022 Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference NeurIPS 2022 Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings ICLR 2022 Orbital MCMC AISTATS 2022 Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems NeurIPS 2022 Maximal Couplings of the Metropolis-Hastings Algorithm AISTATS 2021 Metropolis-Hastings Data Augmentation for Graph Neural Networks NeurIPS 2021 Mixing Time of Metropolis-Hastings for Bayesian Community Detection JMLR 2021 Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019 The Implicit Metropolis-Hastings Algorithm NeurIPS 2019 Scaling up Data Augmentation MCMC via Calibration JMLR 2018 Learning Co-Substructures by Kernel Dependence Maximization IJCAI 2017 On Markov chain Monte Carlo methods for tall data JMLR 2017 Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling JMLR 2016