arXiv Machine Learning
Minimax Optimality of Score-Entropy Discrete Diffusion
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Oct 16
Score Mismatch and Set-Constrained Sampling in Zero-Shot Conditional Discrete Diffusion
The paper presents a theoretical analysis of score mismatch in discrete diffusion models, deriving a KL divergence upper bound that quantifies the bias introduced when the training and target distributions differ. It examines partial-observation constraints such as text infilling and sentence completion, showing that the Conditional SEDD sampler can incur a bias that grows with dimension, vocabulary size, and compatibility. To mitigate this, the authors propose a Randomized Hard Clamping (RHC) sampler for coordinatewise set constraints, demonstrating through experiments that it effectively reduces bias and improves sampling performance.
arXiv Machine Learning
Jul 28
From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models
arXiv:2607. 23226v1 Announce Type: new Abstract: Despite the empirical success of score-based diffusion models, a complete theoretical understanding of how finite-sample learning, network parameterization, and numerical discretization jointly dictate generative quality remains underdeveloped.
arXiv Machine Learning
Jul 1
Efficient Sampling with Discrete Diffusion Models: Sharp and Adaptive Guarantees
arXiv:2602. 15008v2 Announce Type: replace Abstract: Diffusion models over discrete spaces have recently shown striking empirical success, yet their theoretical foundations remain incomplete.
arXiv Machine Learning
Oct 15
Discrete Diffusion Models are Minimax Optimal Under Sparsity
arXiv:2610.19020v1 Announce Type: cross Abstract: Discrete diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their th...
arXiv Machine Learning
Jul 14
Low-dimensional adaptation of diffusion models: Convergence in total variation
arXiv:2501. 12982v3 Announce Type: replace-cross Abstract: This paper investigates how diffusion generative models leverage (unknown) low-dimensional structure to accelerate sampling.
arXiv Machine Learning
Jul 7
Tightening the Score Matching Gap for Diffusion Models
arXiv:2607. 04442v1 Announce Type: cross Abstract: Diffusion models (DMs) are a state-of-the-art generative method to approximately sample from an unknown distribution.