In this article, we study the problem of recovering sparse spikes with over-parametrized
projected descent. We first provide a theoretical study of approximate recovery with our …

We propose a new algorithm for sparse spike estimation from Fourier measurements. Based
on theoretical results on non-convex optimization techniques for off-the-grid sparse spike …

We study the problem of one-dimensional regression of data points with total-variation (TV)
regularization (in the sense of measures) on the second derivative, which is known to …

We consider the problem of recovering off-the-grid spikes from Fourier measurements.
Successful methods such as sliding Frank-Wolfe and continuous orthogonal matching …

We study the problem of interpolating one-dimensional data with total variation
regularization on the second derivative, which is known to promote piecewise-linear …

Non-convex methods for linear inverse problems with low-dimensional models have
emerged as an alternative to convex techniques. We propose a theoretical framework where …

We consider the problem of recovering off-the-grid spikes from linear measurements. The
state of the art Over-Parametrized Continuous Orthogonal Matching Pursuit (OP-COMP) with …

In this letter, we focus on the challenging problem of designing an off-the-grid method for
dictionaries involving both positional and scale shifts. To tackle this challenge, we introduce …

The topic of this Ph. D. thesis lies on the borderline between signal processing, statistics and
computer science. It mainly focuses on compressive learning, a paradigm for large-scale …

Résumé La réplication de l'ADN est un processus hautement régulé dont la dynamique peut
être analysée par des expériences en molécule unique. La vitesse de réplication ie le taux …