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

Episodes

1. 11

   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 the high-dimensional vector of predictors by a lower-dimensional function R(X) with no loss of...

   Dec 3, 2014

   1h 00m
2. 10

   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 is to increase our ability to gain insight and make decisions...

   Dec 3, 2014

   46m
3. 9

   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 mappings of the original data. Recently, it has become clear that a potentially more far...

   Dec 3, 2014

   57m
4. 8

   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 development of adapted statistical tools that allow to analyze such kind of data :...

   Dec 3, 2014

   46m
5. 7

   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 subspace with an intrinsic dimension bounded by the number of clusters. An estimation...

   Dec 3, 2014

   56m
6. 6

   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 to be quantified using probability and for the properties...

   Dec 3, 2014

   1h 04m
7. 5

   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 and overlapping clusterings aim to construct an optimal...

   Dec 3, 2014

   57m
8. 4

   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 focus on representing pathways of biochemical reactions. In social...

   Dec 3, 2014

   54m
9. 3

   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 in the literature for such criteria. These results are asymptotic in the sense that one deals with a...

   Dec 3, 2014

   48m
10. 2

    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. Our goal is to estimate this direction at each arrival of a new block. A simple direct...

    Dec 3, 2014

    50m
11. 1

    4.3 Transfer to an Unlabeled Task using kernel marginal predictors (Gilles Blanchard)

    We consider a classification problem: the goal is to assign class labels to an unlabeled test data set, given several labeled training data sets drawn from different but similar distributions. In essence, the goal is to predict labels from (an estimate of) the marginal distribution (of the...

    Dec 3, 2014

    51m