About
Data sets with a very large number of explanatory variables are becoming more and more common as features of both applications and theoretical investigations. In economical applications for instance, the revealed preference of market players is observed, and the analyst tries to understand them by a complex model by which the players' behavior can be understood as an indirect observation. State-of-the art statistical approaches often formulate such models as inverse problems, but the corresponding methods can suffer of the curse of dimensionality: when there are "too many" possible explanatory variables, additional regularization is needed. Inverse problem theory already offers sophisticated regularization methods for smooth models, but is just beginning to integrate sparsity concepts. For high-dimensional linear models, sparsity regularizations have proved to be a convincing way to tackle the issue both in theory and practice, but there remains a vast ground to be explored. Paralleling the statistics community are also recent advances in machine learning methodology and statistical learning theory, where the themes of sparsity and inverse problems have been intertwined.
The workshop will focus on the different ways to attack a same question: there are many potential models to choose from, but each of them is relatively simple - each model is parameterized by many variables, most of them are zero. Yet, the choice of the right model or regularization parameter is crucial to obtain stable and reliable results.
Find out more about the workshop here.
Videos

Stability Selection for High-Dimensional Data
Dec 18, 2008
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9596 views

Nonparametric estimation of the error distribution in software testing
Dec 18, 2008
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3165 views

Variable selection in nonparametric additive models
Dec 18, 2008
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4669 views

Consistency of random forests and other averaging classifiers
Dec 18, 2008
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7808 views

The incoherence condition in additive models
Dec 18, 2008
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5533 views

Variational Inference and Experimental Design for Sparse Linear Models
Dec 18, 2008
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4472 views

Groupwise sparsity enforcing estimators for solving the EEG/MEG inverse problem
Dec 18, 2008
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4128 views

Sparse Canonical Correlation Analysis
Dec 18, 2008
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6734 views

Some methods of sparse recovery
Dec 18, 2008
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4463 views

Kernel Representations and Kernel Density Estimation
Dec 18, 2008
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6929 views

Approximation of Random Fields in High Dimension
Dec 18, 2008
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3590 views

Statistical performances of SVM Regularization in Classification
Dec 18, 2008
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6691 views

Matching pursuit algorithms in machine learning
Dec 18, 2008
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5809 views

Inverse problems in empirical risk attitudes
Dec 18, 2008
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775 views

Nonnegative garrote in additive models using P-splines
Dec 18, 2008
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4106 views

The prediction error in functional regression
Dec 18, 2008
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4831 views