Seminar: Riccardo Maffucci at Tor Vergata
Dear all, next Thursday, May 5th, Riccardo Maffucci (EPFL) will give a seminar about "Distribution of nodal intersections for random waves". Abstract: This work is in collaboration with Maurizia Rossi. Random waves are Gaussian Laplacian eigenfunctions on the 3D torus. We investigate the length of intersection between the zero (nodal) set, and a fixed surface. Expectation, and variance in a general scenario are prior work. In the generic setting we prove a CLT. We will discuss (smaller order) variance and (non-Gaussian) limiting distribution in the case of ’static’ surfaces (e.g. sphere). Under a certain assumption, there is asymptotic full correlation between intersection length and nodal area. The seminar will take place in Aula De Blasi, Università Tor Vergata, at 16h00. We encourage in-person partecipation, but should you unable to come here is the link to the event on Teams: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... The seminar is part of the Excellence Project Math@TOV. You can find a schedule with the next events at the following link: https://www.mat.uniroma2.it/~rds/events.php .
Cari colleghi, lunedì prossimo, 9 Maggio, l'Università di Tor Vergata avrà il piacere di ospitare Lorenzo Rosasco (Università di Genova) per un colloquium sul machine learning. Il colloquium inaugurerà anche il nuovo centro RoMaDS (Rome Center on Mathematics for Modeling and Data ScienceS) ospitato dal Dipartimento di Matematica dell'università. La lista dei prossimi eventi organizzati del centro è disponibile al link https://www.mat.uniroma2.it/~rds/events.php. L'iniziativa è parte del progetto di eccellenza Math@TOV. 9 Maggio, ore 15h00, aula Dal Passo, Università Tor Vergata A guided tour of machine learning (theory) Speaker: Lorenzo Rosasco Abstract: In this talk, we will provide a basic introduction to some of the fundamental ideas and results in machine learning, with emphasis on mathematical aspects. We will begin contrasting the modern data driven approach to modeling to classic mechanistic approaches. Then, we will discuss basic elements of machine learning theory connected to approximation theory, probability and optimization. Finally, we will discuss the need of new theoretical advances at the light of recent empirical observations while using deep neural networks. Bio: Lorenzo Rosasco is a professor at the University of Genova. He is also visiting professor at the Massachusetts Institute of Technology (MIT) and external collaborator at the Italian Technological Institute (IIT). He coordinates the Machine Learning Genova center (MaLGa) and leads the Laboratory for Computational and Statistical Learning focused on theory, algorithms and applications of machine learning. He received his PhD in 2006 from the University of Genova, after being a visiting student at the Center for Biological and Computational Learning at MIT, the Toyota Technological Institute at Chicago (TTI-Chicago) and the Johann Radon Institute for Computational and Applied Mathematics. Between 2006 and 2013 he has been a postdoc and research scientist at the Brain and Cognitive Sciences Department at MIT. He his a recipient of a number of grants, including a FIRB and an ERC consolidator. Link Teams: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP...
Dear all, the activity of RoMaDS at Tor Vergata (https://www.mat.uniroma2.it/~rds/about.php) will start again next week. On Wednesday, October 26th, at 14h00, Solesne Bourguin (Boston University) will give a seminar about "Regularity of forward-backward SDEs via PDE techniques" Abstract: The study of the regularity of the law of solutions to SDEs is an important and classical topic in stochastic analysis. This was for instance Malliavin's motivation for the development of the stochastic calculus of variations in order to prove a probabilistic version of Hörmander's sum-of-squares theorem. The object of the present work is to study the regularity of solutions to forward-backward SDEs via a novel combination of the Malliavin calculus with PDE techniques such as backward uniqueness and strong unique continuation. We obtain new conditions for the existence of densities of solutions to backward SDEs that not only include all existing results as particular cases, but also allow us to deal with multidimensional forward components. Applications to finance and mathematical biology will be discussed if time permits. The seminar will take place at Tor Vergata University and we encourage in-person partecipation. Should you unable to come, here is the link to the event on Teams: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... The seminar is part of the Excellence Project Math@TOV. You can find a schedule with the next events at the following link: https://www.mat.uniroma2.it/~rds/events.php .
Dear all, On Wednesday, November 2nd, at 14h00 in Aula Dal Passo at Roma Tor Vergata, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Guillaume Poly (University of Rennes 1) with the seminar "Around total variation for Breuer-Major Theorem and nodal volume of Gaussian fields" Abstract: In this talk, I will revisit the Breuer-Major Theorem from the perspective of total variation metric and will introduce some known results recently established in this framework. I will then explain how to break the limitations of these results and establish unconditional criteria of convergence in total variation by using a specific gradient in Malliavin calculus (the sharp operator). Next, i will explain how this kind of ideas may be used in the framework of nodal volume of Gaussian fields in order to establish CLT in the total variation topology. This talk is mainly based on ongoing research with J.Angst and F.Dalmao. We encourage in-person partecipation. Should you unable to come, here is the link to the event on Teams: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... The seminar is part of the Excellence Project Math@TOV. You can find a schedule with the next events at the following link: https://www.mat.uniroma2.it/~rds/events.php .
Dear all, On Wednesday, November 9nd, at 14h00 in Aula Dal Passo at Roma Tor Vergata, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Alessandra Cipriani (UCL, London) with the seminar "Topological data analysis: vineyards for metallic structures" Abstract: Modeling microstructures is a problem that interests material science as well as mathematics. The most basic model for steel microstructure is the Poisson-Voronoi diagram. It has mathematically attractive properties and has been used in the approximation of single-phase steel microstructures. We would like to present methods that can be used to assess whether a real microstructure can be approximated by such a model. In this talk, we construct tests that use data coming from serial sectioning (multiple 2D sections) of a 3D metallic structure. The proposed statistics exploit tools from topological data analysis such as persistence diagrams and (a modified version of) persistence vineyards. ! Due to the hybrid nature of the topic, we would be grateful if you could forward this message to any person interested in Topological Data Analysis in your laboratory ! We encourage in-person partecipation. Should you be unable to come, here is the link to the event on Teams: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... The seminar is part of the Excellence Project Math@TOV. You can find a schedule with the next events at the following link: https://www.mat.uniroma2.it/~rds/events.php .
Dear all, On Wednesday, November 16th, at 14h00 in Aula Dal Passo at Roma Tor Vergata, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Cesare Molinari (IIT and Università di Genova) with the seminar "Iterative regularization for convex regularizers" Abstract: Iterative regularization exploits the implicit bias of an optimization algorithm to regularize ill-posed problems. Constructing algorithms with such built-in regularization mechanisms is a classic challenge in inverse problems but also in modern machine learning, where it provides both a new perspective on algorithms analysis, and significant speed-ups compared to explicit regularization. In this talk, we propose and study the first iterative regularization procedure able to handle biases described by non smooth and non strongly convex functionals, prominent in low-complexity regularization. Our approach is based on a primal-dual algorithm of which we analyze convergence and stability properties, even in the case where the original problem is unfeasible. The general results are illustrated considering the special case of sparse recovery with the ℓ1 penalty. Our theoretical results are complemented by experiments showing the computational benefits of our approach. We encourage in-person partecipation. Should you be unable to come, here is the link to the event on Teams: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... The seminar is part of the Excellence Project Math@TOV. You can find a schedule with the next events at the following link: https://www.mat.uniroma2.it/~rds/events.php .
partecipanti (1)
-
mic sal