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Learning without Smoothness and Strong Convexity

Publications associées (123)

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Activelets and sparsity: A new way to detect brain activation from fMRI data - art. no. 67010Y

Michaël Unser, Dimitri Nestor Alice Van De Ville, Ildar Khalidov

FMRI time course processing is traditionally performed using linear regression followed by statistical hypothesis testing. While this analysis method is robust against noise, it relies strongly on the signal model. In this paper, we propose a non-parametri ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2007

Activelets and Sparsity: A New Way to Detect Brain Activation from fMRI Data

Michaël Unser, Dimitri Nestor Alice Van De Ville, Ildar Khalidov

SPIE2007

Numerical methods and models relevant to transportation applications

Michaël Clément Louis Ghislain Thémans

In this thesis, we focus on standard classes of problems in numerical optimization: unconstrained nonlinear optimization as well as systems of nonlinear equations. More precisely, we consider two types of unconstrained nonlinear optimization problems. On t ...

EPFL2007

Hierarchical Penalization

Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft sel ...

2007

Hierarchical Penalization

IDIAP2007

More Efficiency in Multiple Kernel Learning

An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by \singleemcite{sonnenburg_mkljmlr}. This approach has opened new perspectives since it makes the MKL approach tractable for large-scale problems, by iteratively ...

2007

More Efficiency in Multiple Kernel Learning

IDIAP2007

Prior Knowledge in Kernel Methods

Alexei Pozdnoukhov

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...

École Polytechnique Fédérale de Lausanne2006

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

EPFL2006

Worst-Case Bounds for Gaussian Process Models

Matthias Seeger

We present a competitive analysis of some non-parametric Bayesian algorithms in a worst-case online learning setting, where no probabilistic assumptions about the generation of the data are made. We consider models which use a Gaussian process prior (over ...

2006