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langevin diffusion

23 papers

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Co-occurring keywords

markov chain monte carlo (725) bayesian inference (1907) log-concave distribution (46) stochastic gradient (296) sampling algorithm (130) log-concave sampling (13) wasserstein distance (417) mixing time (70) stochastic process (388) monte carlo sampling (83)

Papers

Sampling and Estimation on Manifolds using the Langevin Diffusion JMLR 2025
Subsampling Error in Stochastic Gradient Langevin Diffusions AISTATS 2024
Fit Like You Sample: Sample-Efficient Generalized Score Matching from Fast Mixing Diffusions COLT 2024
Adaptive Tuning for Metropolis Adjusted Langevin Trajectories AISTATS 2023
Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincaré Inequality COLT 2023
A Complete Recipe for Diffusion Generative Models ICCV 2023
The probability flow ODE is provably fast NIPS 2023
Universality of Langevin Diffusion for Private Optimization, with Applications to Sampling from Rashomon Sets COLT 2023
Bounding the Error of Discretized Langevin Algorithms for Non-Strongly Log-Concave Targets JMLR 2022
Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants NIPS 2022
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm JMLR 2021
SGLB: Stochastic Gradient Langevin Boosting ICML 2021
Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent NIPS 2021
Time-independent Generalization Bounds for SGLD in Non-convex Settings NIPS 2021
Exponential ergodicity of mirror-Langevin diffusions NIPS 2020
On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method NIPS 2020
Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo ICML 2018
Convergence of Langevin MCMC in KL-divergence ALT 2018
Large-Scale Stochastic Sampling from the Probability Simplex NIPS 2018
Global Non-convex Optimization with Discretized Diffusions NIPS 2018
Langevin Monte Carlo and JKO splitting COLT 2018
Log-concave sampling: Metropolis-Hastings algorithms are fast! COLT 2018