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Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its apparent universality. Many results exist on the… (More)

- Sylvain Arlot, Pascal Massart
- Journal of Machine Learning Research
- 2009

Penalization procedures often suffer from their dependence on multiplying factors, whose optimal values are either unknown or hard to estimate from the data. We propose a completely data-driven… (More)

- Sylvain Arlot
- 2008

We present a new family of model selection algorithms based on the resampling heuristics. It can be used in several frameworks, do not require any knowledge about the unknown law of the data, and may… (More)

- Sylvain Arlot
- 2008

We study the efficiency of V -fold cross-validation (VFCV) for model selection from the non-asymptotic viewpoint, and suggest an improvement on it, which we call “V -fold penalization”. Considering a… (More)

- Rémi Lajugie, Francis R. Bach, Sylvain Arlot
- ICML
- 2014

We consider unsupervised partitioning problems based explicitly or implicitly on the minimization of Euclidean distortions, such as clustering, image or video segmentation, and other change-point… (More)

- Sylvain Arlot, Francis R. Bach
- NIPS
- 2009

This paper tackles the problem of selecting among several linear estimators in non-parametric regression; this includes model selection for linear regression, the choice of a regularization parameter… (More)

- Sylvain Arlot, Robin Genuer
- ArXiv
- 2014

Random forests are a very effective and commonly used statistical method, but their full theoretical analysis is still an open problem. As a first step, simplified models such as purely random… (More)

- Rémi Lajugie, Damien Garreau, Francis R. Bach, Sylvain Arlot
- NIPS
- 2014

In this paper, we propose to learn a Mahalanobis distance to perform alignment of multivariate time series. The learning examples for this task are time series for which the true alignment is known.… (More)

- Matthieu Solnon, Sylvain Arlot, Francis R. Bach
- Journal of Machine Learning Research
- 2012

In this paper we study the kernel multiple ridge regression framework, which we refer to as multi-task regression, using penalization techniques. The theoretical analysis of this problem shows that… (More)

- Sylvain Arlot
- 2008

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of… (More)