Seminars appear in decreasing order in relation to date. To find an activity of your interest just go down on the list. Normally seminars are given in english. If not, they will be marked as Spanish Only.


Quantum Mean Field Asymptotics and Multiscale Analysis

Event Date: Jan 22, 2020 in Differential Equations, Seminars

Abstract: In a joint work with Z. Ammari, and F. Nier, we study how multiscale  analysis and quantum mean field asymptotics can be brought together. In  particular we study when a sequence of one-particle density matrices has  a limit with two components: one classical and one quantum. The introduction of “separating quantization for a family” provides a  simple criterion to check when those two types of limit are well  separated. We also give examples of explicit computations of such limits, and how to check that the separating...

Fredholm groupoids

Event Date: Jan 22, 2020 in Differential Equations, Seminars

Resume : In many cases, the study of linear partial differential equations on a singular manifold can be related to that of a Lie groupoid whose action generates the (pseudo)differential operators of interest. Obtaining Fredholm conditions for these operators leads to the definition of Fredholm groupoids as recently introduced by Carvalho, Nistor and Qiao and also studied by Côme. I will introduce theses objects and give examples to illustrate the notion of Fredholm groupoids.  

Le Jugement Majoritaire, une nouvelle théorie du choix social

Event Date: Jan 15, 2020 in Other Areas, Seminars

Abstract: Le Jugement Majoritaireest une nouvelle théorie du choix social applicable à toute prise de décision collective, établie par les chercheurs du CNRS Michel Balinski et Rida Laraki à partir de 2006. En adoptant une toute nouvelle perspective du vote pour tenter de répondre au théorème d’impossibilité d’Arrow, elle résout les paradoxes de l’élection constatés par Condorcet et Arrow. L’électeur vote en évaluant individuellement tous les candidats, à partir d’une échelle commune et ordinale du mentions (par...

The median rule in judgement aggregation (joint work with Klaus Nehring).

Event Date: Jan 15, 2020 in Other Areas, Seminars

Abstract: I will first briefly introduce social choice theory in general. I will then move onto the subfield of judgement aggregation, and discuss some recent research on this topic. In a judgement aggregation problem, we begin with a set K of logically interconnected propositions, called issues. A view is an assignment of a truth-value to each issue in K. However, not all views are admissible; some may violate the logical relationships between the different issues in K. Suppose that each individual voter has a logically consistent view; we...

On the unreasonable effectiveness of the Sinkhorn algorithm

Event Date: Jan 14, 2020 in Seminario Aprendizaje de Máquinas, Seminars

Abstract: This talk concerns Sinkhorn algorithm, broadly understood as the iterative scaling of a matrix that realizes the solution of an entropy regularized linear program subjected to row and column constraints. I will present new theoretical and applied results that demonstrate the effectiveness of this procedure in two contexts: first, Sinkhorn algorithm implements the solution of an entropy regularized version of optimal transport. I will show this regularization substantially improves sample complexity over the unregularized case, a...

Latent distance estimation for random geometric graphs.

Event Date: Jan 07, 2020 in Seminario Aprendizaje de Máquinas, Seminars

Abstract: Random geometric graphs are a popular choice for a latent points generative model for networks. Their definition is based on a sample of $n$ points $X_1,X_2,\cdots,X_n$ on the Euclidean sphere~$\mathbb{S}^{d-1}$ which represents the latent positions of nodes of the network. The connection probabilities between the nodes are determined by an unknown function (referred to as the “link” function) evaluated at the distance between the latent points. We introduce a spectral estimator of the pairwise distance between latent...

An Introduction to Reinforcement Learning and Reward Machines.

Event Date: Jan 07, 2020 in Seminario Aprendizaje de Máquinas, Seminars

In Reinforcement Learning (RL), an agent is guided by the rewards it receives from the reward function. Unfortunately, it may take many interactions with the environment to learn from sparse rewards, and it can be challenging to specify reward functions that reflect complex reward-worthy behavior. We propose using reward machines (RMs), which are automata-based representations that expose reward function structure, as a normal form representation for reward functions. We show how specifications of reward in various formal languages, including...

Interferometria de Radar de Apertura Sintética: teoría y aplicaciones en volcanología, tectónica activa, y deformación antropogénica.

Event Date: Jan 03, 2020 in Geomechanics Laboratory, Seminars

Resumen: En la primera parte se presentarán los conceptos básicos de la interferometría satelital y en la segunda parte se verán algunas aplicaciones a diferentes áreas de la ciencia de la tierra.

Traveling waves for some nonlocal 1D Gross-Pitaevskii equations with nonzero conditions at infinity

Event Date: Dec 18, 2019 in Differential Equations, Seminars

Abstract:   We consider a nonlocal family of Gross-Pitaevskii equations with nonzero condition at infinity in dimension one. In this talk, we provide conditions on the nonlocal interaction such that there is a branch of traveling waves solutions with nonvanishing conditions at infinity. Moreover, we show that the branch is orbitally stable. In this manner, this result generalizes known properties for the short-range interaction case given by a Dirac delta function. Our proof relies on the minimization of the energy at fixed momentum and...

An efficient symmetry breaking technique for arbitrary groups

Event Date: Dec 04, 2019 in AGCO, Seminars

Abstract:  Symmetries are commonly found in Integer Linear Programs (ILPs) or in some of their substructures. Having many symmetric solutions could make common algorithms as branch-and-bound inefficient, hence breaking symmetries might yield important gains. Given a group of symmetries of an ILP, a Fundamental Domain is a set of R^n that aims to select a unique representative of symmetric vectors, i.e. such that each point in the set is a unique representative under its G-orbit, effectively breaking all symmetries of the group. The canonical...