PAC-Bayesian High Dimensional Bipartite Ranking

Benjamin Guedj; Sylvain Robbiano

doi:10.1016/j.jspi.2017.10.010

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arXiv · 1511.02729

PAC-Bayesian High Dimensional Bipartite Ranking

Benjamin Guedj·Sylvain Robbiano

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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.

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BibTeX RIS

Benjamin Guedj, Sylvain Robbiano. 2019-05-16. PAC-Bayesian High Dimensional Bipartite Ranking. https://doi.org/10.1016/j.jspi.2017.10.010

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