Les meilleurs podcasts Higher Education (2021)

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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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1.1 Ultrametric wavelet regression of multivariate time series: application to Colombian conflict analysis (Fionn Murtagh) 47:24

6y ago47:24

47:24

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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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1.2 On the regularization of Sliced Inverse Regression (Stéphane Girard) 49:19

6y ago49:19

49:19

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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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1.3 Simultaneous Gaussian Model-Based Clustering for Samples of Multiple Origins (Christophe Biernacki) 1:00:17

6y ago1:00:17

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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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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2.1 Mixed-Membership Stochastic Block-Models for Transactional Data (Hugh Chipman) 55:10

6y ago55:10

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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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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2.3 A Mixture of Experts Latent Position Cluster Model for Social Network Data (Claire Gormley) 49:47

6y ago49:47

49:47

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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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3.1 Estimator selection with unknown variance (Christophe Giraud) 56:05

6y ago56:05

56:05

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 …

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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3.2 Regularization Methods for Categorical Predictors (Gerhard Tutz) 53:33

6y ago53:33

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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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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3.3 Importance sampling methods for Bayesian discrimination between embedded models (Jean-Michel Marin) 29:46

6y ago29:46

29:46

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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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4.1 High-dimensional interventions and causality : some results and many unsolved problems (Peter Bühlmann) 1:00:38

6y ago1:00:38

1:00:38

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…

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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4.2 Statistical analysis of bio-molecular data and combinatorial difficulties : two examples (Stéphane Robin) 51:37

6y ago51:37

51:37

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 …

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StatLearn 2010 - Workshop on "Challenging problems in Statistical Learning"

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4.3 Data-driven penalties for optimal calibration of learning algorithms (Sylvain Arlot) 1:09:50

6y ago1:09:50

1:09:50

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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HELARY Matthieu