Skip to Main content Skip to Navigation
Journal articles

PAC-Bayesian High Dimensional Bipartite Ranking

Benjamin Guedj 1, * Sylvain Robbiano 2
* Corresponding author
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
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.
Complete list of metadata

Cited literature [8 references]  Display  Hide  Download

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

File

GuedjRobbiano2015.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

Identifiers

Collections

Citation

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

Share

Metrics

Record views

679

Files downloads

675