StatLearn 2012 - Workshop on "Challenging problems in Statistical Learning"

3 décembre 2014 · 1 h

1.1 Dimension reduction based on finite mixture modeling of inverse regression (Luca Scrucca)

Consider the usual regression problem in which we want to study the conditional distribution of a response Y given a set of predictors X. Sufficient dimension reduction (SDR) methods aim at replacing...

3 décembre 2014 · 46 min 33 s

1.2 Information Visualization: An Introduction to the Field and Applications for Statistics (Petra Isenberg)

Information visualization is a research area that focuses on making structures and content of large and complex data sets visually understandable and interactively analyzable. The goal of information visualization tools and techniques...

3 décembre 2014 · 57 min 28 s

2.1 Hypothesis Testing and Bayesian Inference: New Applications of Kernel Methods (Arthur Gretton)

In the early days of kernel machines research, the "kernel trick" was considered a useful way of constructing nonlinear learning algorithms from linear ones, by applying the linear algorithms to feature space...

3 décembre 2014 · 46 min 15 s

2.2 Functional estimation in high dimensional data : Application to classification (Sophie Dabo-Niang)

Functional data are becoming increasingly common in a variety of fields. Many studies underline the importance to consider the representation of data as functions. This has sparked a growing attention in the...

3 décembre 2014 · 56 min 8 s

2.3 Discriminative clustering for high-dimensional data (Camille Brunet)

A new family of 12 probabilistic models, introduced recently, aims to simultaneously cluster and visualize high-dimensional data. It is based on a mixture model which fits the data into a latent discriminative...

3 décembre 2014 · 1 h 4 min

3.1 Exploring Clustering Structure in Ranking Data (Brendan Murphy)

Cluster analysis is concerned with finding homogeneous groups in a population. Model-based clustering methods provide a framework for developing clustering methods through the use of statistical models. This approach allows for uncertainty...

3 décembre 2014 · 57 min 3 s

3.2 Co-clustering under different approaches (Mohamed Nadif)

Cluster analysis is an important tool in a variety of scientific areas including pattern recognition, document clustering, and the analysis of microarray data. Although many clustering procedures such as hierarchical, strict partitioning...

3 décembre 2014 · 54 min 8 s

3.3 Complexity control in overlapping stochastic block models (Pierre Latouche)

Networks are highly used to represent complex systems as sets of interactions between units of interest. For instance, regulatory networks can describe the regulation of genes with transcriptional factors while metabolic networks...

3 décembre 2014 · 48 min 32 s

4.1 Data-driven penalties: heuristics, results and thoughts... (Pascal Massart)

The idea of selecting a model via penalizing a log-likelihood type criterion goes back to the early seventies with the pioneering works of Mallows and Akaike. One can find many consistency results...

3 décembre 2014 · 50 min 11 s

4.2 A sliced inverse regression approach for block-wise evolving data streams (Jérôme Saracco)

In this communication, we focus on data arriving sequentially by block in a stream. A semiparametric regression model involving a common EDR (Effective Dimension Reduction) direction B is assumed in each block....