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# Résultats de recherche

**9679**

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## G. Binyamini - Point counting for foliations over number fields

We consider an algebraic $V$ variety and its foliation, both defined over a number field. Given a (compact piece of a) leaf $L$ of the foliation, and a subvariety $W$ of complementary codimension, we give an upper bound for the number of intersections between $L$ and $W$. The bound depends polynomially on the degree of $W$, the logarithmic height of $W$, and the logarithmic distance between $L$ and the locus of points where leafs of the foliation intersect $W$ improperly. Using this theory ... Voir la vidéole (41m54s)

## Amis de l'Instruction : émancipez-vous, lisez !

avec Michel Blanc, maître de conférences honoraire Université Paris-X Nanterre, Secrétaire général de la Bibliothèque des amis de l'Instruction (BAI).Voir, en complément, le site internet de la BAI et sa très riche audiothèque (podcasts) de conférences régulièrement organisées à la bibliothèque — qui poursuit ainsi depuis 1860 son rôle originel d'instruction et de diffusion de la connaissance : http://bai.asso.fr/wordpress/soirees-de-lecture/kiosque-a-conferences/ Voir la vidéole (51m21s)

## D. Novikov - Wilkie's conjecture for restricted elementary functions

We consider the structure $\mathbb{R}^{RE}$ obtained from $(\mathbb{R}, Voir la vidéole (1h36s)

## H. Guenancia - A decomposition theorem for singular spaces with trivial canonical class (Part 2)

The Beauville-Bogomolov decomposition theorem asserts that any compact Kähler manifold with numerically trivial canonical bundle admits an étale cover that decomposes into a product of a torus, an irreducible, simply-connected Calabi-Yau, and holomorphic symplectic manifolds. With the development of the minimal model program, it became clear that singularities arise as an inevitable part of higher dimensional life. We will present recent works in which a singular version of the decomposition theorem is established. Voir la vidéole (1h3m4s)

## Bubeck 5/9 - Some geometric aspects of randomized online decision making

This course is concerned with some of the canonical non-stochastic models of online decision making. These models have their origin in works from the 1950's and 1960's, and went through a resurgence in the mid-2000's due to many applications in the internet economy. This course focuses on a set of challenging conjectures around these models from the 1980's and 1990's. We present a unified approach based on a combination of convex optimization techniques together with powerful probabilistic tools, which will allow us to derive state of the art results in online learning, bandit optimization, as well as some classical online computing ... Voir la vidéole (48m4s)

## Bubeck 6/9 - Some geometric aspects of randomized online decision making

This course is concerned with some of the canonical non-stochastic models of online decision making. These models have their origin in works from the 1950's and 1960's, and went through a resurgence in the mid-2000's due to many applications in the internet economy. This course focuses on a set of challenging conjectures around these models from the 1980's and 1990's. We present a unified approach based on a combination of convex optimization techniques together with powerful probabilistic tools, which will allow us to derive state of the art results in online learning, bandit optimization, as well as some classical online computing ... Voir la vidéole (56m22s)

## Bubeck 7/9 - Some geometric aspects of randomized online decision making

This course is concerned with some of the canonical non-stochastic models of online decision making. These models have their origin in works from the 1950's and 1960's, and went through a resurgence in the mid-2000's due to many applications in the internet economy. This course focuses on a set of challenging conjectures around these models from the 1980's and 1990's. We present a unified approach based on a combination of convex optimization techniques together with powerful probabilistic tools, which will allow us to derive state of the art results in online learning, bandit optimization, as well as some classical online computing ... Voir la vidéole (49m53s)

## Zdeborová - Loss landscape and behaviour of algorithms in the spiked matrix-tensor model

A key question of current interest is: How are properties of optimization and sampling algorithms influenced by the properties of the loss function in noisy high-dimensional non-convex settings? Answering this question for deep neural networks is a landmark goal of many ongoing works. In this talk I will answer this question in unprecedented detail for the spiked matrix-tensor model. Information theoretic limits, and Kac-Rice analysis of the loss landscapes, will be compared to the analytically studied performance of message passing algorithms, of the Langevin dynamics and of the gradient flow. Several rather non-intuitive results will be unveiled and explained. Voir la vidéole (1h1m44s)