13 citations · 18 across the 4 of their papers we have counts for
8 papers
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…
Quantitative Propagation of Chaos for SGD in Wide Neural Networks
Valentin De Bortoli, Alain Durmus, Xavier Fontaine +1
In this paper, we investigate the limiting behavior of a continuous-time counterpart of the Stochastic Gradient Descent (SGD) algorithm applied to two-layer overparameterized neura…
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…
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part I: Methodology and Experiments
Ana F. Vidal, Valentin De Bortoli, Marcelo Pereyra +1
Many imaging problems require solving an inverse problem that is ill-conditioned or ill-posed. Imaging methods typically address this difficulty by regularising the estimation prob…
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation
Valentin De Bortoli, Alain Durmus, Marcelo Pereyra +1
Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as marginal likelihoods arising in p…
Patch redundancy in images: a statistical testing framework and some applications
De Bortoli Valentin, Desolneux Agnès, Galerne Bruno +1
In this work we introduce a statistical framework in order to analyze the spatial redundancy in natural images. This notion of spatial redundancy must be defined locally and thus w…