S
Stéphan Clémençon
Researcher at Télécom ParisTech
Publications - 215
Citations - 3000
Stéphan Clémençon is an academic researcher from Télécom ParisTech. The author has contributed to research in topics: Ranking & Empirical risk minimization. The author has an hindex of 24, co-authored 208 publications receiving 2597 citations. Previous affiliations of Stéphan Clémençon include Institut national de la recherche agronomique & Centre national de la recherche scientifique.
Papers
More filters
Posted Content
Ranking and empirical minimization of U-statistics
TL;DR: This paper forms the ranking problem in a rigorous statistical framework, establishes in particular a tail inequality for degenerate U-processes, and applies it for showing that fast rates of convergence may be achieved under specific noise assumptions, just like in classification.
Journal ArticleDOI
Ranking and Empirical Minimization of U-statistics
TL;DR: In this article, the authors formulate the ranking problem in a rigorous statistical framework, where the goal is to learn a ranking rule for deciding, among two instances, which one is "better" with minimum ranking risk.
Journal Article
Ranking the Best Instances
TL;DR: A local form of the bipartite ranking problem where the goal is to focus on the best instances and a methodology based on the construction of real-valued scoring functions which involve empirical quantiles of the scores is proposed.
Book ChapterDOI
Ranking and scoring using empirical risk minimization
TL;DR: This work investigates learning methods based on empirical minimization of the natural estimates of the ranking risk of U-statistics and U-processes to give a theoretical framework for ranking algorithms based on boosting and support vector machines.
Journal ArticleDOI
Tree-Based Ranking Methods
TL;DR: This paper investigates how recursive partitioning methods can be adapted to the bipartite ranking problem and describes committee-based learning procedures using TreeRank as a ldquobase ranker in order to overcome obvious drawbacks of such a top-down partitioning technique.