6 papers
Trajectory inference via Acceleration Matching
Bartolo Dazzini, Giovanni Conforti, Alain Durmus +1
Trajectory inference is a fundamental problem in many scientific domains: given a collection of unpaired snapshots of observations at discrete time points, the goal is to generate…
Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees
Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus
Diffusion Flow Matching (DFM) has recently emerged as a versatile framework for generative modeling, yet its theoretical convergence properties remain only partially understood. In…
Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
Lucas Morisset, Alain Durmus, Adrien Hardy
This paper aims at analyzing the regularization effect that data augmentation induces on supervised regression methods in the proportional regime, where the number of covariates gr…
Discrete Flow Matching: Convergence Guarantees Under Minimal Assumptions
Le-Tuyet-Nhi Pham, Giovanni Conforti, Zhenjie Ren +1
Flow Matching has recently emerged as a popular class of generative models for simulating a target distribution from samples drawn from a source distribution . This fr…
Non-Asymptotic Analysis of Data Augmentation for Precision Matrix Estimation
Lucas Morisset, Adrien Hardy, Alain Durmus
This paper addresses the problem of inverse covariance (also known as precision matrix) estimation in high-dimensional settings. Specifically, we focus on two classes of estimators…
On the Rate of Gaussian Approximation for Linear Regression Problems
Marat Khusainov, Marina Sheshukova, Alain Durmus +1
In this paper, we consider the problem of Gaussian approximation for the online linear regression task. We derive the corresponding rates for the setting of a constant learning rat…