Researcher @ CNRS, Paris
Homepage: CNRS-INRIA and ENS
Lecture 1. (Monday February 14) Statistical learning
- the statistical learning learning problem
- examples: prediction, regression, classification, density estimation
- estimators: definition, consistency, examples
- universal learning rates and No Free Lunch Theorems 
- the estimator selection paradigm, bias-variance decomposition of the risk
- data-driven selection procedures and the unbiased risk estimation principle
Lecture 2. (Tuesday February 15) Model selection for least-squares regression
- ideal penalty, Mallows' Cp
- oracle inequality for Cp (i.e., non-asymptotic optimality of the corresponding model selection procedure), corresponding learning rates 
- the variance estimation problem
- minimal penalties and data-driven calibration of penalties: the slope heuristics [3,4]
- algorithmic and other practical issues 
Lecture 3. (Thursday February 17) Linear estimator selection for least-squares regression 
- linear estimators: (kernel) ridge regression, smoothing splines, k-nearest neighbours, Nadaraya-Watson estimators
- bias-variance decomposition of the risk
- the linear estimator selection problem: CL penalty
- oracle inequality for CL
- data-driven calibration of penalties: a new light on the slope heuristics
Lecture 4. (Tuesday February 22) Resampling and model selection
- regressograms in heteroscedastic regression: the penalty cannot be a function of the dimensionality of the models 
- resampling in statistics: general heuristics, the bootstrap, exchangeable weighted bootstrap 
- study of a case example: estimating the variance by resampling
- resampling penalties: why do they work for heteroscedastic regression? oracle-inequality. comparison of the resampling weights 
Lecture 5. (Wendsday February 23) Cross-validation and model/estimator selection 
- cross-validation: principle, main examples
- cross-validation for estimating of the prediction risk: bias, variance
- cross-validation for selecting among a family of estimators: main properties, how should the splits be chosen?
- illustration of the robustness of cross-validation: detecting changes in the mean of a signal with unknown and non-constant variance 
- correcting the bias of cross-validation: V-fold penalization. Oracle-inequality. 
 Luc Devroye, Laszlo Gyorfi, and Gabor Lugosi. A probabilistic theory of pattern recognition, volume 31 of
Applications of Mathematics (New York). Springer-Verlag, New York, 1996.
 Pascal Massart. Concentration Inequalities and Model Selection, volume 1896 of Lecture Notes in Mathematics.
Springer, Berlin, 2007. Lectures from the 33rd Summer School on Probability Theory held in Saint-Flour, July 6-23, 2003.
 Lucien Birge and Pascal Massart. Minimal penalties for Gaussian model selection. Probab. Theory Related Fields, 138(1-2):33-73, 2007.
 Sylvain Arlot and Pascal Massart. Data-driven calibration of penalties for least-squares regression. J. Mach. Learn. Res., 10:245-279 (electronic), 2009. http://jmlr.csail.mit.edu/papers/v10/arlot09a.html
 Jean-Patrick Baudry, Cathy Maugis, and Bertrand Michel. Slope Heuristics : Overview and Implementation.
Technical Report 7223, INRIA, 2010. http://hal.archives-ouvertes.fr/hal-00461639/en/
 Sylvain Arlot and Francis Bach. Data-driven calibration of linear estimators with minimal penalties. Proceedings of NIPS 2009. http://arxiv.org/abs/0909.1884
 Sylvain Arlot. Choosing a penalty for model selection in heteroscedastic regression. Preprint. 2010. http://arxiv.org/abs/0812.3141
 Bradley Efron and Robert J. Tibshirani. An Introduction to the Bootstrap, volume 57 of Monographs on
Statistics and Applied Probability. Chapman and Hall, New York, 1993.
 Sylvain Arlot. Model selection by resampling penalization. Electronic Journal of Statistics, 3, (2009), 557-624 (electronic). http://dx.doi.org/10.1214/08-EJS196
 Sylvain Arlot and Alain Celisse. A survey of cross-validation procedures for model selection. Statist. Surv., 4:40-79, 2010. http://dx.doi.org/10.1214/09-SS054
 Sylvain Arlot and Alain Celisse. Segmentation of the mean of heteroscedastic data via cross-validation. Statistics and Computing, 2010. http://arxiv.org/abs/0902.3977
 Sylvain Arlot. V-fold cross-validation improved: V-fold penalization. Preprint. 2008. http://fr.arxiv.org/abs/0802.0566