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mcmc sampling

70 papers

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Co-occurring keywords

markov chain monte carlo (725) bayesian inference (1907) hamiltonian monte carlo (92) mixing time (70) energy-based model (251) posterior inference (138) markov chain (207) riemannian manifold (169) determinantal point process (140) gibbs sampling (201)

Papers

A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning AAAI 2025
Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains (Extended Abstract) IJCAI 2024
Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions JMLR 2024
A Dynamical System View of Langevin-Based Non-Convex Sampling NIPS 2023
Distance-to-Set Priors and Constrained Bayesian Inference AISTATS 2023
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo NIPS 2023
Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach NIPS 2023
Discrete Langevin Samplers via Wasserstein Gradient Flow AISTATS 2023
Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models - and a Deep-learning Solution AISTATS 2022
Discrete Probabilistic Inverse Optimal Transport ICML 2022
Joint 3D Object Detection and Tracking Using Spatio-Temporal Representation of Camera Image and LiDAR Point Clouds AAAI 2022
Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling JMLR 2022
Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data AISTATS 2022
Identifying Norms from Observation Using MCMC Sampling IJCAI 2021
Pseudo-Marginal Hamiltonian Monte Carlo JMLR 2021
Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo ICML 2021
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling NIPS 2021
Optimal Underdamped Langevin MCMC Method NIPS 2021
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients ICML 2021
Aggregated Gradient Langevin Dynamics AAAI 2020
Learning Latent Space Energy-Based Prior Model NIPS 2020
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models AAAI 2020
Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach AISTATS 2019
Flexible Modeling of Diversity with Strongly Log-Concave Distributions NIPS 2019