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cs.LG2026
Convergence rates for generative drifting flows: fixed-scale obstructions and multihead acceleration
Arthur Stéphanovitch, Eddie Aamari
Drifting models offer a promising route to faster generative AI: they perform gradual transport during training, while generating new samples in a single step. This paper asks whet…
math.ST2026
Statistical Analysis of Markovian Generative Modeling
Eddie Aamari, Arthur Stéphanovitch
These lecture notes introduce the statistical analysis of continuous-time generative models built from Markov dynamics. We begin with the stochastic-calculus foundations of score-b…
math.ST2026
Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities
Arthur Stéphanovitch
Under general assumptions on the target distribution , we establish a sharp Lipschitz regularity theory for flow-matching vector fields and diffusion-model scores, with op…