Sampling from Unnormalized Distributions

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21 papers4 papers in the last four weeks, down 33% on 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
WASABI: Wasserstein Sampling via Bridge Interpolants
Oct 30, 2026Sebastian Salazar, Dilum Fernando, Michal Kucer +3Boltzmann Sampling Sampling from Unnormalized Distributions
Columbia University · Los Alamos National Laboratory · Johns Hopkins University
MiniMMD: Minimization of MMD for Sampling, Inference and Integration
Oct 28, 2026Zonghao ChenMaximum Mean Discrepancy Sampling from Unnormalized Distributions
Anchored Stein Variational Gradient Descent Algorithm for Non-smooth Sampling
Oct 26, 2026Thanh Dang, Matthew X. Burns, Jiaming LiangStein Variational Gradient Descent Sampling from Unnormalized Distributions
Department of Computer Science, University of Rochester, Rochester, NY 14620. · Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627 · Goergen Institute for Data Science and Artificial Intelligence and Department of Computer Science, University of Rochester, Rochester, NY 14620
Neural Sampling with Reweighted Normalizing Flows via the Wasserstein--Fisher--Rao JKO Scheme
Oct 7, 2026Chenguang Duan, Johannes Hertrich, Gabriele SteidlSampling from Unnormalized Distributions Boltzmann Sampling
Institut für Geometrie und Praktische Mathematik, RWTH Aachen University · Institute of Computer Science, University of Göttingen · Institute of Mathematics, TU Berlin
Score-Calibrated Flow for Sampling from Unnormalized Densities with Applications to Generative Online Reinforcement Learning
Oct 3, 2026Zeyang Li, Yunan Wang, Risheek Garrepalli +2Sampling from Unnormalized Distributions Flow-Based Generative Modeling
Massachusetts Institute of Technology · Qualcomm AI Research
Iterative Exact Discrete Guidance for Energy-Based Sampling
Sep 29, 2026Yuwen Qian, Yidong Ouyang, Zhengyan Wan +1Sampling from Unnormalized Distributions Adaptive Sampling
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen · Department of Statistics, University of California, Los Angeles · School of Data Science, The Chinese University of Hong Kong, Shenzhen
Uniform Race: Parameter-Free Approximate Rejection Sampling
Sep 28, 2026Seiyun Shin, Juhyeong Pang, Kwang-Sung JunRejection Sampling Sampling from Unnormalized Distributions
Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, 37673, South Korea · Department of Computer Science, University of Wisconsin–Madison, Madison, WI 53706, USA · Graduate School of Artificial Intelligence, Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, 37673, South Korea
FUND: Density Flow for Sampling Unnormalised Distributions
Sep 26, 2026Vikas Kanaujia, Vipul AroraSampling from Unnormalized Distributions Boltzmann Sampling
Department of Electrical Engineering IIT Kanpur · Department of Electrical Engineering KU Leuven
SIMANF: Sample Free Learning of Unnormalized Distributions via Simulated Annealing in Normalizing Flows
Sep 26, 2026Vikas KanaujiaLattice Field Theory Sampling from Unnormalized Distributions
Mahindra University Department of Electrical and Computer Engineering Hyderabad, Telangana, India
Bridging Stochastic Flow Maps and Boltzmann Generators with Normalizing Flows
Sep 25, 2026Louis Grenioux, RuiKang OuYang, Luhuan WuBoltzmann Sampling Molecular Generation
Center for Computational Mathematics, Flatiron Institute, New York, NY, USA · Department of Engineering, University of Cambridge, Cambridge, UK · Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
Mode Collapse Is Cheap to Detect: A Ground-Truth-Free Pre-Flight Check for Neural Samplers
Aug 20, 2026Jian XuMode Collapse Sampling from Unnormalized Distributions
RIKEN iTHEMS, Wako, Japan · RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows
May 18, 2026Chenguang Wang, Tianshu YuWasserstein Gradient Flows One-Step Generative Modeling
School of Data Science The Chinese University of Hong Kong, Shenzhen
Sobolev Regularized MMD Gradient Flow
May 12, 2026Chenyang Tian, Bharath K. Sriperumbudur, Arthur Gretton +1Maximum Mean Discrepancy Sampling from Unnormalized Distributions
1Tsinghua University · 2Pennsylvania State University · University College London +1
Conditional Diffusion Sampling
May 5, 2026Francisco M. Castro-Macías, Pablo Morales-Álvarez, Saifuddin Syed +3Markov Chain Monte Carlo Sampling from Unnormalized Distributions
University of Granada · Work done while visiting the University of Cambridge. · University of British Columbia +2
Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
May 5, 2026Aaron Havens, Brian Karrer, Neta ShaulSampling from Unnormalized Distributions Diffusion Sampling
FAIR at Meta · Weizmann Institute of Science
Total Variation Guarantees for Sampling with Stochastic Localization
Mar 31, 2026Jakob KellermannSampling from Unnormalized Distributions Diffusion Sampling
Weierstrass Institute Berlin
One-Step Sampler for Boltzmann Distributions via Drifting
Mar 18, 2026Wenhan Cao, Keyu Yan, Lin ZhaoBoltzmann Sampling Sampling from Unnormalized Distributions
Department of Electrical and Computer Engineering, National University of Singapore
A Generative Sampler for distributions with possible discrete parameter based on Reversibility
Mar 10, 2026Lei Li, Zhen Wang, Lishuo ZhangSampling from Unnormalized Distributions Generative Modeling
School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, P.R.China. · Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University, Shanghai, 200240, P.R.China.
Control Variate Score Matching for Diffusion Models
Dec 23, 2025Khaled Kahouli, Romuald Elie, Klaus-Robert Müller +3Score Matching Sampling from Unnormalized Distributions
1Google DeepMind · 2BIFOLD – Berlin Institute for the Foundations of Learning and Data · 3Machine Learning Group, Technische Universität Berlin +2
Data-to-Energy Stochastic Dynamics
Sep 30, 2025Kirill Tamogashev, Esmeralda S. WhitammerSampling from Unnormalized Distributions Schrödinger Bridges
University of Edinburgh

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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.