Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : This paper is devoted to the bipartite ranking problem, a classical statistical learning task, in a high dimensional setting. We propose a scoring and ranking strategy based on the PAC-Bayesian approach. We consider nonlinear additive scoring functions, and we derive non-asymptotic risk bounds under a sparsity assumption. In particular, oracle inequalities in probability holding under a margin condition assess the performance of our procedure, and prove its minimax optimality. An MCMC-flavored algorithm is proposed to implement our method, along with its behavior on synthetic and real-life datasets.
https://hal.inria.fr/hal-01226472
Contributeur : Benjamin Guedj
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Soumis le : mardi 10 novembre 2015 - 13:23:58
Dernière modification le : mercredi 14 novembre 2018 - 14:40:11
Document(s) archivé(s) le : vendredi 12 février 2016 - 17:15:02
Benjamin Guedj, Sylvain Robbiano. PAC-Bayesian High Dimensional Bipartite Ranking. Journal of Statistical Planning and Inference, Elsevier, 2018, 〈10.1016/j.jspi.2017.10.010〉. 〈hal-01226472〉
