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 o

4.3 Data-driven penalties for optimal calibration of learning algorithms (Sylvain Arlot)

Dec 4th, 2014 1h 9m

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. F

4.2 Statistical analysis of bio-molecular data and combinatorial difficulties : two examples (Stéphane Robin)

Dec 4th, 2014 51m 37s

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 qu

3.1 Estimator selection with unknown variance (Christophe Giraud)

Dec 4th, 2014 56m 5s

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 b

1.1 Ultrametric wavelet regression of multivariate time series: application to Colombian conflict analysis (Fionn Murtagh)

Dec 4th, 2014 47m 24s

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

1.2 On the regularization of Sliced Inverse Regression (Stéphane Girard)

Dec 4th, 2014 49m 19s

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 the

4.1 High-dimensional interventions and causality : some results and many unsolved problems (Peter Bühlmann)

Dec 4th, 2014 1h

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 associat

3.2 Regularization Methods for Categorical Predictors (Gerhard Tutz)

Dec 4th, 2014 53m 33s

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 wil

2.2 Visualization of graphs by organized clustering : application to social and biological networks (Nathalie Villa-Vialaneix)

Dec 4th, 2014 57m 11s

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

2.3 A Mixture of Experts Latent Position Cluster Model for Social Network Data (Claire Gormley)

Dec 4th, 2014 49m 47s

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 n

1.3 Simultaneous Gaussian Model-Based Clustering for Samples of Multiple Origins (Christophe Biernacki)

Dec 4th, 2014 1h

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 b

3.3 Importance sampling methods for Bayesian discrimination between embedded models (Jean-Michel Marin)

Dec 4th, 2014 29m 46s

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 r

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