88 citations · 487 across the 78 of their papers we have counts for
5 papers · 1 filter
KL Convergence Guarantees for Score diffusion models under minimal data assumptions
Giovanni Conforti, Alain Durmus, Marta Gentiloni Silveri
Diffusion models are a new class of generative models that revolve around the estimation of the score function associated with a stochastic differential equation. Subsequent to its…
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis
Valentin De Bortoli, Alain Durmus, Ana F. Vidal +1
This paper presents a detailed theoretical analysis of the three stochastic approximation proximal gradient algorithms proposed in our companion paper [49] to set regularization pa…
Maximum entropy methods for texture synthesis: theory and practice
Valentin De Bortoli, Agnes Desolneux, Alain Durmus +2
Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis. These methods often rely on the minimization of some variational formu…
High-dimensional Bayesian inference via the Unadjusted Langevin Algorithm
Alain Durmus, Eric Moulines
We consider in this paper the problem of sampling a high-dimensional probability distribution having a density with respect to the Lebesgue measure on , known up…
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm
Alain Durmus, Eric Moulines
In this paper, we study a method to sample from a target distribution over having a positive density with respect to the Lebesgue measure, known up to a normalis…