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PAC-Bayesian high dimensional bipartite ranking

Guedj, B; Robbiano, S; (2018) PAC-Bayesian high dimensional bipartite ranking. Journal of Statistical Planning and Inference , 196 pp. 70-86. 10.1016/j.jspi.2017.10.010. Green open access

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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.

Type: Article
Title: PAC-Bayesian high dimensional bipartite ranking
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.jspi.2017.10.010
Publisher version: https://doi.org/10.1016/j.jspi.2017.10.010
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Bipartite ranking, High dimension and sparsity, MCMCPAC-Bayesian aggregation, Supervised statistical learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10074723
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