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Le ministère de l'enseignement supérieur et de la recherche amis en place depuis 2004 une certification informatique et internetpermettant d'attester de compétences dans la maîtrise des outilsinformatiques et réseaux.Cette certification est instituée dansle but de développer, de renforcer et de valider la maîtrise destechnologies de l’information et de la communication par les étudiantsen formation dans les établissements d’enseignement supérieur.Il est prévu deux niveaux :• un niveau 1 d’ex ...
 
L'apprentissage statistique joue de nos jours un rôle croissant dans de nombreux domaines scientifiques et doit de ce fait faire face à des problèmes nouveaux. Il est par conséquent important de proposer des méthodes d'apprentissage statistique adaptées aux problèmes modernes posés par les différents champs d'application. Outre l'importance de la précision des méthodes proposées, elles devront également apporter une meilleure compréhension des phénomènes observés. Afin de faciliter les conta ...
 
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A growing number of applicative fields generate data that are pairwise relations between the objects under study instead of attributes associated to every object : social networks (relations between persons), biology (interactions between genes, proteins), www (relations between websites or blogs), marketing (relations between customers and service…
 
We first pursue the study of how hierarchy provides a well-adapted tool for the analysis of change. Then, using a time sequence-constrained hierarchical clustering, we develop the practical aspects of a new approach to wavelet regression. This provides a new way to link hierarchical relationships in a multivariate time series data set with external…
 
Sliced Inverse Regression (SIR) is an effective method for dimension reduction in highdimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a regular…
 
Mixture model-based clustering usually assumes that the data arise from a mixture population in order to estimate some hypothetical underlying partition of the dataset. In this work, we are interested in the case where several samples have to be clustered at the same time, that is when the data arise not only from one but possibly from several mixt…
 
Transactional network data arise in many fields. Although social network models have been applied to transactional data, these models typically assume binary relations between pairs of nodes. We develop a latent mixed membership model capable of modelling richer forms of transactional data. Estimation and inference are accomplished via a variationa…
 
Social network data represent the interactions between a group of social actors. Interactions between colleagues and friendship networks are typical examples of such data. The latent space model for social network data locates each actor in a network in a latent (social) space and models the probability of an interaction between two actors as a fun…
 
We consider the problem of Gaussian regression (possibly in a high- dimensional setting) when the noise variance is unknown. We propose a procedure which selects within any collection of estimators, an estimator hatf that nearly achieves the best bias/variance trade off. This selection procedure can be used as an alternative to Cross Validation to …
 
The majority of regularization methods in regression analysis has been designed for metric predictors and can not be used for categorical predictors. A rare exception is the group lasso which allows for categorical predictors or factors. We will consider alternative approaches based on penalized likelihood and boosting techniques. Typically the ope…
 
We survey some approaches on the approximation of Bayes factors used in Bayesian model choice and propose a new one. Our focus here is on methods that are based on importance sampling strategies, rather than variable dimension techniques like reversible jump MCMC, including : crude Monte Carlo, MLE based importance sampling, bridge and harmonic mea…
 
Understanding cause-effect relationships between variables is of interest in many fields of science. To effectively address such questions, we need to look beyond the framework of variable selection or importance from models describing associations only. We will show how graphical modeling and intervention calculus can be used for quantifying inter…
 
Combinatorial issues are often raised by statistical model inference and selection, in particular when dealing with high-dimensional data. In such cases, asymptotic approximations or Monte-Carlo type methods are often used to approximate the quantities of interest. In this talk, we will present two examples dealing with bio-molecular data. In both …
 
Learning algorithms usually depend on one or several parameters that need to be chosen carefully. We tackle in this talk the question of designing penalties for an optimal choice of such regularization parameters in non-parametric regression. First, we consider the problem of selecting among several linear estimators, which includes model selection…
 
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