most citedAnalysis of Diffusion Models for Manifold Data

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Denoising Score Matching in Diffusion Models: The Effect of Rank Constraints on Generalization

Anand Jerry George, Nicolas Macris

Architectural biases restrict the class of functions that a diffusion score network can represent, but their effect on generalization remains poorly understood. We study denoising…

cs.LG2026

Asymptotic Learning Curves for Diffusion Models with Random Features Score and Manifold Data

Anand Jerry George, Nicolas Macris

We study the theoretical behavior of denoising score matching--the learning task associated to diffusion models--when the data distribution is supported on a low-dimensional manifo…

math.ST2025★ 2 cited

Analysis of Diffusion Models for Manifold Data

Anand Jerry George, Rodrigo Veiga, Nicolas Macris

We analyze the time reversed dynamics of generative diffusion models. If the exact empirical score function is used in a regime of large dimension and exponentially large number of…

cs.LG2025

Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations

Anand Jerry George, Nicolas Macris

We present a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions given their unnormalized densities. Ideally, our methods can transp…

cs.LG2025

Denoising Score Matching with Random Features: Insights on Diffusion Models from Precise Learning Curves

Anand Jerry George, Rodrigo Veiga, Nicolas Macris

We theoretically investigate the phenomena of generalization and memorization in diffusion models. Empirical studies suggest that these phenomena are influenced by model complexity…