Mardi 12 Janvier
Heure: 
12:30  13:30 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
An Exact Semidefinite Programming Approach for the MaxMean Dispersion Problem 
Description: 
Michele Garraffa This work proposes an exact algorithm for the MaxMean Dispersion Problem (MaxMean DP), an NPhard combinatorial optimization problem whose aim is to select the subset of a set such that the average distance between elements is maximized. The problem admits a natural nonconvex quadratic fractional formulation from which a semidefinite programming (SDP) relaxation can be derived. In general, semidefinite relaxations have been successfully used to determine tight (but usually computationally expensive) lower/upper bounds for challenging NPhard problems, such as the Quadratic Assignment Problem and the Quadratic Knapsack Problem. The semidefinite relaxation for the MaxMean DP can be further tightened by means of a cutting plane algorithm which iteratively adds the most violated triangular inequalities. The proposed approach embeds the SDP relaxation and the cutting plane algorithm into a branch and bound framework to solve MaxMean DP instances to optimality. To authors' knowledge, this is the first exact algorithm that has been proposed for this problem. Computational experiments show that the proposed method is able to solve to optimality in reasonable time instances with up 100 elements, while standard methods for fractional combinatorial optimization manage instances whose size is less then or equal to 75. The presented approach can be generalized to other combinatorial problems with a quadratic fractional objective and linear constraints. 
Heure: 
14:00  17:00 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
TBC 
Description: 
Julien Courtiel 
Mercredi 13 Janvier
Heure: 
14:00  17:00 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
TBC [attention, c'est un mercredi] 
Description: 
Julien Courtiel 
Lundi 18 Janvier
Heure: 
14:00  15:00 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
Deep lexical segmentation and syntactic parsing in the easyfirst dependency framework 
Description: 
Joseph Le Roux 
Jeudi 21 Janvier
Heure: 
14:00  17:00 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
The CRT is the scaling limit of large random dissections 
Description: 
Bénédicte Haas 
Heure: 
14:30  17:30 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
L'opération de flip dans les triangulations : du stockage de données à la topologie des surfaces 
Description: 
Lionel Pournin 
Jeudi 28 Janvier
Heure: 
12:15  13:30 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
Non negative matrix factorization for transfer learning 
Description: 
Ievgen Redko The ability of a human being to extrapolate previously gained knowledge to other domains inspired a new family of methods in machine learning called transfer learning. Transfer learning is often based on the assumption that objects in both target and source domains share some common feature and/or data space. If this assumption is false, most of transfer learning algorithms are likely to fail. In this work, we propose to investigate the problem of transfer learning from both theoretical and applicational points of view.
First, we introduce a theoretical framework based on the HilbertSchmidt embeddings that allows us to improve the current stateoftheart theoretical results on transfer learning by introducing a natural and intuitive distance measure with strong computational guarantees for its estimation. The proposed results combine the tightness of datadependent bounds derived from Rademacher learning theory while ensuring the efficient estimation of its key factors.
We also present two different methods to solve the problem of unsupervised transfer learning based on Nonnegative matrix factorization techniques. First one represents a linear approach that aims at discovering an embedding for two tasks that decreases the distance between the corresponding probability distributions while preserving the nonnegativity property. Second one proceeds using an iterative optimization procedure that aims at aligning the kernel matrices calculated based on the data from two tasks. 

