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Journal Articles Journal of Statistical Planning and Inference Year : 2018

PAC-Bayesian High Dimensional Bipartite Ranking

Sylvain Robbiano
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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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hal-01226472 , version 1 (10-11-2015)


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Benjamin Guedj, Sylvain Robbiano. PAC-Bayesian High Dimensional Bipartite Ranking. Journal of Statistical Planning and Inference, 2018, ⟨10.1016/j.jspi.2017.10.010⟩. ⟨hal-01226472⟩
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