Cover Image

Convergence Analysis of Discrete Diffusion Model: Exact Implementation through Uniformization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Hongrui, Ying, Lexing
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
by: Ren, Yinuo, et al.
Published: (2024)

Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis
by: Zhang, Zikun, et al.
Published: (2024)

Multidimensional unstructured sparse recovery via eigenmatrix
by: Ying, Lexing
Published: (2024)

Tangent differential privacy
by: Ying, Lexing
Published: (2024)

A note on continuous-time online learning
by: Ying, Lexing
Published: (2024)

A Unified Approach to Analysis and Design of Denoising Markov Models
by: Ren, Yinuo, et al.
Published: (2025)

Eigenmatrix for unstructured sparse recovery
by: Ying, Lexing
Published: (2023)

Structural and Convergence Analysis of Discrete-Time Denoising Diffusion Probabilistic Models
by: Nakano, Yumiharu
Published: (2024)

Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms
by: Ren, Yinuo, et al.
Published: (2025)

DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models
by: Ren, Yinuo, et al.
Published: (2025)

Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity
by: Chen, Haoxuan, et al.
Published: (2024)

What Exactly Does Guidance Do in Masked Discrete Diffusion Models
by: Ye, He, et al.
Published: (2025)

From Scores to Gibbs Correctors: Accelerating Uniform-Rate Discrete Diffusion Models
by: Liang, Yuchen, et al.
Published: (2026)

Distillation of Discrete Diffusion by Exact Conditional Distribution Matching
by: Gao, Yansong, et al.
Published: (2025)

Understanding the Generalization Benefits of Late Learning Rate Decay
by: Ren, Yinuo, et al.
Published: (2024)

Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Models
by: Liang, Yuchen, et al.
Published: (2025)

List Sample Compression and Uniform Convergence
by: Hanneke, Steve, et al.
Published: (2024)

Uniform Convergence Beyond Glivenko-Cantelli
by: Devale, Tanmay, et al.
Published: (2025)

Ensemble-Based Annealed Importance Sampling
by: Chen, Haoxuan, et al.
Published: (2024)

Sampling on Metric Graphs
by: Dwaraknath, Rajat Vadiraj, et al.
Published: (2025)

Discrete Guidance Matching: Exact Guidance for Discrete Flow Matching
by: Wan, Zhengyan, et al.
Published: (2025)

Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space
by: Kan, Kelvin, et al.
Published: (2026)

Natural Hypergradient Descent: Algorithm Design, Convergence Analysis, and Parallel Implementation
by: Kong, Deyi, et al.
Published: (2026)

UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
by: Wang, Jiaqi, et al.
Published: (2026)

Bit-Level Discrete Diffusion with Markov Probabilistic Models: An Improved Framework with Sharp Convergence Bounds under Minimal Assumptions
by: Pham, Le-Tuyet-Nhi, et al.
Published: (2025)

Logical Guidance for the Exact Composition of Diffusion Models
by: Alesiani, Francesco, et al.
Published: (2026)

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach
by: Chen, Haoxuan, et al.
Published: (2025)

Polynomial Convergence of Riemannian Diffusion Models
by: Xu, Xingyu, et al.
Published: (2026)

Distillation of Discrete Diffusion through Dimensional Correlations
by: Hayakawa, Satoshi, et al.
Published: (2024)

Informed Correctors for Discrete Diffusion Models
by: Zhao, Yixiu, et al.
Published: (2024)

Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
by: Ying, Bicheng, et al.
Published: (2025)

Discrete Diffusion Models: Novel Analysis and New Sampler Guarantees
by: Liang, Yuchen, et al.
Published: (2025)

Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration
by: Yu, Yifeng, et al.
Published: (2025)

Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty
by: Liu, Kaizhao, et al.
Published: (2024)

Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learning
by: Chen, Chuangtao, et al.
Published: (2025)

Latent Shadows: The Gaussian-Discrete Duality in Masked Diffusion
by: Chen, Guinan, et al.
Published: (2026)

Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance
by: Beyler, Eliot, et al.
Published: (2025)

Sketch Tomography: Hybridizing Classical Shadow and Matrix Product State
by: Tang, Xun, et al.
Published: (2025)

Instance-dependent Convergence Theory for Diffusion Models
by: Jiao, Yuchen, et al.
Published: (2024)

Sharp Convergence Rates for Masked Diffusion Models
by: Liang, Yuchen, et al.
Published: (2026)