Évènements à venir

UQSay #01

The first UQSay seminar, organized by L2S, will take place in the afternoon of March 21, 2019, at CentraleSupelec Paris-Saclay (Eiffel building, amphi IV).  We will have two talks: 14h – Mickaël Binois (INRIA Sophia-Antipolis)  [slides] Heteroskedastic Gaussian processes for simulation experiments An increasing number of time-consuming simulators exhibit a complex noise structure that depends […]


Bilevel optimisation approaches for learning the optimal noise model in mixed and non-standard image denoising applications

Speaker — Luca Calatroni (CMAP, École Polytechnique) Abstract — The regularised formulation of a general ill-posed inverse problem in imaging typically combines an edge-preserving regularisation term (like the Total Variation semi-norm) and a data fitting function encoding noise statistics balanced against each other by a positive – possibly space-variant – weight. The optimal choice of […]


A dual certificates analysis of compressive off-the-grid recovery

Speaker — Nicolas Keriven (Ecole Normale Supérieure) Abstract — Many problems in machine learning and imaging can be framed as an infinite dimensional Lasso problem to estimate a sparse measure. This includes for instance regression using a continuously parameterized dictionary, mixture model estimation and super-resolution of images. To make the problem tractable, one typically sketches […]


On an incorrect entry of Gradshteyn and Ryzhik

Speaker — Victor H. Moll (Dept. of Mathematics, Tulane University, New Orleans, USA) Abstract — In the process of verifying entries of the classical table of integrals by Gradshteyn and Ryzhik, the author observed that entry 3.248.5 was incorrect. This talk will discuss how was this discovered, the correct solution obtained this year by Arias […]


High-dimensional covariance matrix estimation with applications to microarray studies and portfolio optimization

Speaker — Esa Ollila (Aalto University and Oulu University, Finland) Abstract — We consider the problem of estimating a high-dimensional (HD) covariance matrix that can be applied in commonly occurring sparse data problems, i.e., when the sample size is smaller or not much larger than the dimensionality of the data, which is potentially very large. […]