Estimateur par agrégat en apprentissage statistique en grande dimension

This thesis explores properties of estimations procedures related to aggregation in the problem of high-dimensional regression in a sparse setting. The exponentially weighted aggregate (EWA) is well studied in the literature. It benefits from strong results in fixed and random designs with a PAC-Bayesian approach. However, little is known about the properties of the EWA with Laplace prior. Chapter 2 analyses the statistical behaviour of the prediction loss of the EWA with Laplace prior in the fixed design setting. Sharp oracle inequalities which generalize the properties of the Lasso to a larger family of estimators are established. These results also bridge the gap from the Lasso to the Bayesian Lasso. Chapter 3 introduces an adjusted Langevin Monte Carlo sampling method that approximates the EWA with Laplace prior in an explicit finite number of iterations for any targeted accuracy. Chapter 4 explores the statisctical behaviour of adjusted versions of the Lasso for the transductive and semi-supervised learning task in the random design setting.Les travaux de cette thèse explorent les propriétés de procédures d'estimation par agrégation appliquées aux problèmes de régressions en grande dimension. Les estimateurs par agrégation à poids exponentiels bénéficient de résultats théoriques optimaux sous une approche PAC-Bayésienne. Cependant, le comportement théorique de l'agrégat avec extit{prior} de Laplace n'est guère connu. Ce dernier est l'analogue du Lasso dans le cadre pseudo-bayésien. Le Chapitre 2 explicite une borne du risque de prédiction de cet estimateur. Le Chapitre 3 prouve qu'une méthode de simulation s'appuyant sur un processus de Langevin Monte Carlo permet de choisir explicitement le nombre d'itérations nécessaire pour garantir une qualité d'approximation souhaitée. Le Chapitre 4 introduit des variantes du Lasso pour améliorer les performances de prédiction dans des contextes partiellement labélisés

ScreenSeed as a novel high throughput seed germination phenotyping method

International audienceA high throughput phenotyping tool for seed germination, the ScreenSeed technology, was developed with the aim of screening genotype responsiveness and chemical drugs. This was presently used with Arabidopsis thaliana seeds to allow characterizing seed samples germination behavior by incubating seeds in 96-well microplates under defined conditions and detecting radicle protrusion through the seed coat by automated image analysis. This study shows that this technology provides a fast procedure allowing to handle thousands of seeds without compromising repeatability or accuracy of the germination measurements. Potential biases of the experimental protocol were assessed through statistical analyses of germination kinetics. Comparison of the ScreenSeed procedure with commonly used germination tests based upon visual scoring displayed very similar germination kinetics