Proximal Sampling

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8 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Subpolynomial-Query Proximal Sampling Beyond Log-Concavity
Nov 2, 2026Wei Guo, Bo Yuan, Ye He +3Proximal Sampling Sampling from Unnormalized Distributions
Georgia Institute of Technology · University of Colorado Boulder · University of Rochester
Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling
Oct 12, 2026Bo Yuan, Wei Guo, Yongxin ChenProximal Sampling Log-Concave Sampling
Georgia Institute of Technology
Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler
Sep 23, 2026Stefan OberdörsterMarkov Chain Monte Carlo Proximal Sampling
Institute for Applied Mathematics, University of Bonn
Accelerated High-Accuracy Sampling from a Warm Start via the Proximal Bouncy Particle Sampler
Sep 7, 2026Fan Chen, Sinho Chewi, Jianfeng Lu +1Proximal Sampling Markov Chain Monte Carlo
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. · Department of Statistics and Data Science, Yale University. · Department of Mathematics, Duke University. +1
Complexity of Non-Log-Concave Sampling in Fisher Information
May 15, 2026Sinho Chewi, Andre WibisonoOracle Complexity Proximal Sampling
Yale University, Department of Statistics and Data Science. · Yale University, Department of Computer Science.
A proximal gradient algorithm for composite log-concave sampling
May 12, 2026Linghai Liu, Sinho ChewiProximal Sampling Log-Concave Sampling
Yale University
Alternating Diffusion for Proximal Sampling with Zeroth Order Queries
Mar 20, 2026Hirohane Takagi, Atsushi NitandaProximal Sampling Diffusion Sampling
Graduate School of Information Science and Technology, The University of Tokyo, Japan · Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore +1
Constrained and Composite Sampling via Proximal Sampler
Feb 16, 2026Thanh Dang, Jiaming LiangProximal Sampling Constrained Sampling
Department of Computer Science, University of Rochester, Rochester, NY 14620. · Goergen Institute for Data Science and Artificial Intelligence (GIDS-AI) and Department of Computer Science, University of Rochester, Rochester, NY 14620

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Subpolynomial-Query Proximal Sampling Beyond Log-Concavity

Wei Guo, Bo Yuan, Ye He, Jiaming Liang+2

Georgia Institute of Technology · University of Colorado Boulder · University of Rochester

Proximal Sampling Sampling from Unnormalized Distributions

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We study the gradient query complexity of sampling from a density

π(x)\propto\mathrm{e}^{-V(x)}

\mathbb{R}^d

, where

V

has a

β

-Lipschitz gradient but need not be convex. Our approach uses the proximal sampler, which alternates Gaussian perturbation with sampling a position conditional on the noisy observation. We implement its observation chain using the high-order noisy conditional sampler of [OpenAI 2026], obtaining improved dimension dependence under functional inequalities. If

π

satisfies a log-Sobolev inequality (LSI) with constant

α

, then for every fixed

ν>0

, there is a finite constant

C_ν

depending only on

ν

such that the algorithm returns a sample within total-variation distance

\varepsilon

using at most

C_νκ(d+κ+\varepsilon^{-1}+\|\nabla V(x_0)\|^2/β)^ν

queries, where

κ=β/α

and

x_0

is an arbitrary reference point for initialization. The analysis controls center-dependent local errors along exact proximal histories and removes the final observation noise by conditional decoding. Under a Poincaré inequality (PI), we obtain a corresponding guarantee from a warm start with a known bound on its Rényi divergence. An oracle lower bound shows that a stationary point alone, which suffices under LSI, is insufficient under PI.