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PAC-Bayesian High Dimensional Bipartite Ranking
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.
Cited literature [8 references]
https://hal.inria.fr/hal-01226472
Contributor : Benjamin Guedj <>
Submitted on : Tuesday, November 10, 2015 - 1:23:58 PM
Last modification on : Friday, May 28, 2021 - 3:58:03 PM
Long-term archiving on: : Friday, February 12, 2016 - 5:15:02 PM
Contributor : Benjamin Guedj <>
Submitted on : Tuesday, November 10, 2015 - 1:23:58 PM
Last modification on : Friday, May 28, 2021 - 3:58:03 PM
Long-term archiving on: : Friday, February 12, 2016 - 5:15:02 PM
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GuedjRobbiano2015.pdf
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