Score-Based Generative Modeling

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6 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

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Sample complexity bounds for categorical Markov random fields via Discrete Diffusions
Oct 1, 2026Shivam Kumar, Nabarun DebMarkov Random Fields Diffusion Sampling
Booth School of Business, University of Chicago
Weighting Schedules Govern What and When Score-Based Generative Models Learn from Multimodal Data
Sep 28, 2026Jérémie Klinger, Raphaël Urfin, Giulio Biroli +1Generative Modeling Score-Based Generative Modeling
Laboratoire de Physique de l’École normale supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris Cité, F-75005 Paris, France
Quantum score matching with applications to learning thermal states
Sep 23, 2026Yulong Dong, Jiaqi LengQuantum Machine Learning Score-Based Generative Modeling
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, USA · Institute for Advanced Computing, Virginia Tech, Alexandria, Virginia 22305, USA · Department of Computer Science, Virginia Tech, Alexandria, Virginia 22305, USA
Robustness of Diffusion Models under Distribution Shift
Sep 23, 2026Wei Luo, Neil K. Chada, Shijie Zhang +1Distribution Shift Robustness Diffusion Sampling
Department of Mathematical Sciences, Tsinghua University · Department of Mathematics, City University of Hong Kong, Hong Kong SAR · Artificial Intelligence Research Institute, Shenzhen MSU-BIT University +1
Walking the Score Manifold: Continuous-time Generative Dynamics on Learned Data Manifolds
Sep 15, 2026Jan Tauberschmidt, Brian B. Moser, Stanislav Frolov +3Dynamical Systems Latent Dynamics Modeling
DFKI Kaiserslautern · RPTU Kaiserslautern · Imperial College London
Score-based Outlier Generation via Controlling the Radon-Nikodym Derivative
Sep 14, 2026Amartya Mukherjee, Tristan Milne, Kry Yik-Chau Lui +2Diffusion Sampling Synthetic Anomaly Generation
Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 · Royal Bank of Canada, 1 Place Ville Marie, Montreal, Quebec, H3C 3A9
Intrinsic-Hybrid Latent Diffusion Models for Generative Modeling on Unknown Manifolds
Aug 5, 2026Yizhu Wang, Mu Niu, Xiaochen YangLatent Diffusion Models Generative Modeling
School of Mathematics and Statistics, University of Glasgow, Glasgow, U.K.
A Hyperfinite Framework for Score-Based Generative Modeling
Aug 3, 2026Sunder Ram KrishnanScore Matching Stochastic Differential Equations
Kerala School of Mathematics, Kozhikode 673571, Kerala, India.
Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regression
Jul 30, 2026Silas KoemenFlow Matching Gradient-Boosted Decision Trees
Independent Researcher
From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models
Jul 25, 2026Jinshu Huang, Yiming Jiang, Chunlin WuDiffusion Model Sampling Score-Based Generative Modeling
School of Mathematical Sciences, Nankai University, Tianjin, China
From Score Approximation to Distribution Approximation in Score-Based Diffusion Models
Jul 24, 2026Lan V. TruongScore-Based Generative Modeling Diffusion Models
Faculty of Computer Science and Engineering · Ho Chi Minh City University of Technology (HCMUT) · Vietnam National University Ho Chi Minh City (VNU-HCM), Vietnam
Toward a mechanistic understanding of inference in visual cortex and diffusion models
Jul 17, 2026Zeyu Yun, Alexander Belsten, Dasheng Bi +3Mechanistic Interpretability Visual Neuroscience
Dept. of Electrical Engineering and Computer Science, UC Berkeley · Redwood Center for Theoretical Neuroscience, UC Berkeley · Herbert Wertheim School of Optometry and Vision Science, UC Berkeley +3
Quantum Circuits in Diffusion Models: A Fair-Comparison Study and a Mechanistic Analysis of Angle-Embedding Failures
Jul 10, 2026Jaeuk Kim, Sanghoon YooQuantum Generative Modeling Variational Quantum Circuits
NextITS Co., Ltd.
Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
Jul 7, 2026Robert Gruhlke, Julius Berner, David Sommer +1Stochastic Differential Equations Diffusion Sampling
Freie Universität Berlin · NVIDIA · WIAS +2
Non-Asymptotic Error Bounds for SMC with Biased Proposals: Application to Conditional Diffusion Sampling
Jul 6, 2026Stanislas Strasman, Gabriel Victorino Cardoso, Sylvain Le Corff +2Markov Chain Monte Carlo Markov Models
SU, LPSM · Sorbonne Université and Université Paris Cité, CNRS, LPSM, F-75005 Paris, France · LPSM +2
Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
Jul 6, 2026Stanislas Strasman, Sobihan Surendran, Sylvain Le CorffNonconvex Stochastic Optimization Score-Based Generative Modeling
SU, LPSM
Tightening the Score Matching Gap for Diffusion Models
Jul 5, 2026Benjamin Dupuis, Tyler Farghly, Maxime Haddouche +2Score Matching Score-Based Generative Modeling
INRIA - CNRS - D´epartement d’Informatique de l’Ecole Normale Sup´erieure - PSL Research University, France · Ecole Polytechnique - CMAP, IP Paris, Palaiseau, France
Class-frequency Guided Noise Schedule for Diffusion Models
Jun 26, 2026Jiequan Cui, Beier Zhu, Qingshan Xu +3Image Generation Score-Based Generative Modeling
Hefei University of Technology. · University of Science and Technology of China. · The University of Hong Kong. +2
Sampling the Schwinger Model with Gauge-Equivariant Diffusion
Jun 25, 2026Octavio Vega, Aida X. El-KhadraDiffusion Sampling Generative Modeling
Department of Physics University of Illinois Urbana-Champaign Urbana, IL 61801
LISA: Likelihood Score Alignment for Visual-condition Controllable Generation
Jun 25, 2026Yanghao Wang, Hongxu Chen, Jiazhen Liu +4Score-Based Generative Modeling Controllable Video Generation
The Hong Kong University of Science and Technology · Huawei Research
Laplace-Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation
Jun 23, 2026Alois Duston, Tan Bui-ThanhBayesian Inference Score-Based Generative Modeling
The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin · The Oden Institute for Computational Engineering and Sciences, and the Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin
$Ω$ : Operator-based Mixture Ensemble for Generative Assimilation
Jun 18, 2026Pouria Behnoudfar, Nan ChenData Assimilation Posterior Sampling
University of Wisconsin-Madison, Department of Mathematics, Madison, WI, USA
Score Approximation for Diffusion Models on Arbitrary Low-Dimensional Structures
Jun 18, 2026Xinhe Mu, Zaijiu Shang, Zhaoqi Zhou +4Neural Network Approximation Theory Score-Based Generative Modeling
Academy of Mathematics and Systems Sciences Chinese Academy of Sciences Beijing, China · Shanghai Institute for Mathematics and Interdisciplinary Sciences Shanghai, China · Huawei Technologies Co., Ltd. Beijing, China
Volterra Generative Models
Jun 16, 2026Yusen Jia, Bingyan HanDiffusion Sampling Diffusion-Based Image Generation
The Hong Kong University of Science and Technology (Guangzhou)
Exact Posterior Score Estimation for Solving Linear Inverse Problems
Jun 15, 2026Abbas Mammadov, Ozgur Kara, Kaan Oktay +5Diffusion Models for Image Restoration Diffusion-Based Inverse Problems
University of Oxford · UIUC · fal +1
A Perception vs. Distortion Perspective on Score-Based Generative Channel Estimation
Jun 15, 2026Marco Skocaj, Lukas Eller, Mate BobanWireless Communications Channel Estimation
All authors are with the Advanced Wireless Technology Laboratory, Huawei Heisenberg Research Centre, Munich, Germany.
When Do Local Score Models Extrapolate Across Size? A Diagnostic Theory and Benchmark
Jun 8, 2026Wenjie XiScore-Based Generative Modeling
Department of Physics and HK Institute of Quantum Science & Technology The University of Hong Kong, Pokfulam Road, Hong Kong, China
Where the Score Lives: A Wavelet View of Diffusion
Jun 6, 2026Emma Finn, Binxu Wang, T. Anderson Keller +1Score-Based Generative Modeling Diffusion Models
The Kempner Instutite for the Study of Natural and Artificial Intelligence Harvard University, Cambridge, MA 15213
Diffusion Models Observe Only Gradients: A Geometric Perspective on Score Matching Errors
Jun 4, 2026Naïl B. Khelifa, Richard E. Turner, Ramji VenkataramananScore Matching Hodge Decomposition
University of Cambridge
Learning Implicit Bias in Generative Spaces for Accelerating Protein Dynamics Emulation
Jun 1, 2026Kaihui Cheng, Zhiqiang Cai, Wenkai Xiang +4Diffusion Model Sampling Adaptive Sampling
1Fudan University · 2Shanghai Academy of AI for Science · 3Shanghai Innovation Institute
Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
May 29, 2026Gyeonghoon Ko, Juho LeePhysics-Informed Diffusion Models Physics-Informed Generative Modeling
1Korea Advanced Institute of Science and Technology, Korea.
The Score Hamiltonian: Mapping Diffusion Models to Adiabatic Transport
May 28, 2026Peter Halmos, Boris HaninDiffusion Sampling Score-Based Generative Modeling
Computer Science Department, Princeton University. · ORFE Department, Princeton University.
GenSBI: Generative Methods for Simulation-Based Inference in JAX
May 26, 2026Aurelio AmerioFlow Matching Simulation-Based Inference
Instituto de Física Corpuscular (IFIC) Universitat de València & CSIC València, Spain
Rao-Blackwellized Score Matching on Manifolds
May 25, 2026Divit RawalScore Matching Score-Based Generative Modeling
Department of Statistics, University of California, Berkeley, Berkeley CA, U.S.A..
Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation
May 21, 2026Benedikt Lütke Schwienhorst, Nadja Klein, Johannes LedererScore Matching Parameter Estimation
Department of Mathematics University of Hamburg Hamburg, Germany · Scientific Computing Center Karlsruhe Institute of Technology2026 Karlsruhe, Germany · Department of MathematicsMay University of Hamburg Hamburg, Germany21
A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models
May 21, 2026Jiayi Fu, Yuxia WangDiffusion Model Sampling Score-Based Generative Modeling
INSAIT, Sofia University “St. Kliment Ohridski”, Bulgaria
Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
May 21, 2026Ziyuan Zhu, Keyu Hu, Zhifei Chen +10Physics-Informed Generative Modeling Physics-Informed ML
School of Advanced Manufacturing Engineering, Nanjing University, Suzhou, 215163, China. · National Laboratory of Solid State Microstructures, Nanjing University, Nanjing, 210093, China. · Suzhou Acoustics Industry Technology Research Institute Co., Ltd., Suzhou, 215513, China. +8
Tweedie's Formulae and Diffusion Generative Models Beyond Gaussian
May 19, 2026Wenpin Tang, Nizar Touzi, Zikun Zhang +1Stochastic Differential Equations Time Series Generation
Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics
May 18, 2026Benjamin Sterling, Mónica F. Bugallo, Tom TirerLangevin Dynamics Memorization in Generative Models
Department of Applied Math & Statistics Stony Brook University Stony Brook, NY 11790 · Department of Electrical and Computer Engineering Stony Brook University Stony Brook, NY 11790 · Faculty of Engineering Bar-Ilan University Ramat-Gan 5290002, Israel
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
May 16, 2026Wei Huang, Andi Han, Mingyuan Bai +4Latent Diffusion Models Manifold Learning
RIKEN AIP & The Institute of Statistical Mathematics · University of Sydney · Agency for Science, Technology and Research & The Institute of Statistical Mathematics +4
Towards Generalized Image Manipulation Localization via Score-based Model
May 16, 2026Yunfei Wang, Bo Du, Zhe Yang +4Image Forgery Detection Image Forgery Localization
Sichuan University · Chengdu, China
Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds
May 15, 2026Guoji Fu, Taiji Suzuki, Wee Sun Lee +1Intrinsic Dimensionality Generative Modeling
National University of Singapore · The University of Tokyo · RIKEN Center for Advanced Intelligence Project +2
Proximal-Based Generative Modeling for Bayesian Inverse Problems
May 13, 2026Boyang Zhang, Zhiguo Wang, Ya-Feng LiuScore Matching Bayesian Inverse Problems
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · School of Mathematics, Sichuan University, Chengdu, China +2
Free Energy Manifold: Score-Based Inference for Hybrid Bayesian Networks
May 11, 2026Cheol Young Park, Shou MatsumotoBayesian Networks Energy-Based Models
ATOS Co., Ltd., 273, Digital-ro, Guro-gu, Seoul, 08381, Republic of Korea · C5I Center, George Mason University, Fairfax, VA 22030, USA
Learning Unified Representations of Normalcy for Time Series Anomaly Detection
May 10, 2026Prithul Sarker, Sushmita Sarker, Nicholas G. Murray +1Reconstruction-Based Anomaly Detection Unsupervised Anomaly Detection
University of Nevada, Reno
Metropolis-Adjusted Diffusion Models
May 10, 2026Kevin H. Lam, Tyler Farghly, Christopher Williams +3Markov Chain Monte Carlo Diffusion Sampling
Department of Statistics, University of Oxford · Inria, CNRS, École Normale Supérieure, PSL Research University · Department of Mathematical Sciences, University of Copenhagen
Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
May 8, 2026Atsushi Nitanda, Dake Bu, Yueming Lyu +1Diffusion Model Guidance Langevin Dynamics
Agency for Science, Technology and Research (A⋆STAR) · Nanyang Technological University · City University of Hong Kong
Generative Modeling with Flux Matching
May 8, 2026Peter Pao-Huang, Xiaojie Qiu, Stefano ErmonGenerative Modeling Score-Based Generative Modeling
Department of Computer Science, Stanford University · Department of Genetics, Stanford University
Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
May 7, 2026Victor Livernoche, Jie Zan, Reihaneh RabbanyTabular Anomaly Detection Unsupervised Anomaly Detection
McGill University, Mila, Montréal, Canada
A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models
May 7, 2026Aditya Ranganath, Mukesh SinghalFlow Matching Generative Modeling
Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore, CA 94551, USA · Department of Electrical Engineering University of California Merced, CA 95343, USA
The Interplay of Data Structure and Imbalance in the Learning Dynamics of Diffusion Models
May 7, 2026Flavio Nicoletti, Chenxiao Ma, Enrico Ventura +2Class-Imbalanced Learning Score-Based Generative Modeling
Data Science and AI, Computer Science and Engineering Department, Chalmers University of Technology and University of Gothenburg · International School of Advanced Studies (SISSA), Trieste, Italy · Department of Computing Sciences, Bocconi University, Milano, Italy +2
Predictive-Generative Drift Decomposition for Speech Enhancement and Separation
May 7, 2026Julius Richter, Yoshiki Masuyama, Christoph Boeddeker +3Stochastic Interpolants Speech Processing
MERL Cambridge, MA, USA
Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation
May 5, 2026Jian Xu, Wei Chen, Shigui Li +5Quantum Generative Modeling Quantum State Preparation
1RIKEN iTHEMS · 2RIKEN AIP · 3South China University of Technology +2
A unified perspective on fine-tuning and sampling with diffusion and flow models
Apr 30, 2026Carles Domingo-Enrich, Yuanqi Du, Michael S. AlbergoStochastic Optimal Control Diffusion Sampling
Microsoft Research New England · Harvard University, Kempner Institute
Quantum Dynamics via Score Matching on Bohmian Trajectories
Apr 28, 2026Lei WangScore Matching PDE Solving
Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
Symmetric Equilibrium Propagation for Thermodynamic Diffusion Training
Apr 26, 2026Aditi DeEquilibrium Propagation Thermodynamic Computing
Score-Based Matching with Target Guidance for Cryo-EM Denoising
Apr 20, 2026Xiaoqi Wu, Xueying Zhan, Wen Li +3Cryo-Electron Microscopy Score-Based Generative Modeling
Carnegie Mellon University · Towson University
Reward Score Matching: Unifying Reward-based Fine-tuning for Flow and Diffusion Models
Apr 19, 2026Jeongjae Lee, Jinho Chang, Jeongsol Kim +1Diffusion Model Alignment Diffusion Model Fine-Tuning
Graduate School of AI KAIST, Korea
An Analysis of Regularization and Fokker-Planck Residuals in Diffusion Models for Image Generation
Apr 16, 2026Onno Niemann, Gonzalo Martínez Muñoz, Alberto Suárez GonzalezDiffusion-Based Image Generation Score-Based Generative Modeling
Universidad Autónoma de Madrid
Asymptotic Learning Curves for Diffusion Models with Random Features Score and Manifold Data
Mar 24, 2026Anand Jerry George, Nicolas MacrisIntrinsic Dimensionality Score-Based Generative Modeling
École Polytechnique Fédérale de Lausanne (EPFL), Lab for Statistical Mechanics of Inference in Large Systems (SMILS), CH-1015 Lausanne, Switzerland

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Sample complexity bounds for categorical Markov random fields via Discrete Diffusions

Shivam Kumar, Nabarun Deb

Booth School of Business, University of Chicago

Markov Random Fields Diffusion Sampling Score-Based Generative Modeling Diffusion Models

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Many applications in statistics, economics, and physics require sampling from high-dimensional categorical distributions with local dependence structures. Examples include finite memory language models, Ising and Potts systems in statistical physics and protein folding, etc. In modern machine learning, discrete diffusions have emerged as a flexible approach for sampling such data, with strong empirical performance. Motivated by this, we develop learning methods with end-to-end sample complexity bounds for discrete diffusion with uniform noising under local dependence, which we model through low order Markov random fields (MRFs). Our main technical insight is a new \emph{pinning decomposition} of the discrete score. It shows that unlike in continuous diffusions, the score decomposes into components where the dependence on time separates multiplicatively from the dependence on the target. Building on this decomposition, we propose a \emph{weight-sharing neural score learner} and combine it with

τ

-leaping to obtain an end-to-end sampling procedure. Rather than treating score-learning error as a black-box input, as is common in existing sampling analyses, we study the score learning error from finite data and derive optimal sampling guarantees with explicit dependence on the vocabulary size, the interaction order of the MRF, and the sample size. Moreover, our strategy trains a single score network across uniform noise levels while leaving the sampling discretization to be chosen at inference-time. This allows the same trained model to trade accuracy for computational cost as inference-time budgets vary. Numerical experiments on Potts, Ising, and tree-structured models show that weight-sharing score networks outperform fully connected ones for sampling long sequences.

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