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.
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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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