Upcomming events

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. […]


A new fast and robust bootstrap method for statistical inference in ICA using the FastICA

Speaker — Shahab Basiri (Department of Signal Processing and Acoustics, Aalto University, Finland) Abstract — Independent component analysis (ICA) is a widely used signal processing technique in extracting unobserved independent source signals from their observed multivariate mixture recordings. The FastICA fixed-point algorithm is one of the most popular ICA algorithms. In this talk, we develop […]


Séminaire de Claudio Gaz

14:00-15:00 Claudio Gaz (Post-Doc researcher, Department of Computer, Control and Management Engineering (DIAG), Sapienza Università di Roma, Italy) Title.  The role of modelling and parameter identification for controlling robotic and biological systems. Abstract. Mathematical models are widely employed to describe phenomena in diverse domains, as natural or social sciences, or engineering. While model-less approaches (i.e., machine learning […]