TY  - JOUR
TI  - PAC-Bayesian high dimensional bipartite ranking
Y1  - 2018/08//
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
PB  - ELSEVIER SCIENCE BV
ID  - discovery10074723
UR  - https://doi.org/10.1016/j.jspi.2017.10.010
VL  - 196
A1  - Guedj, B
A1  - Robbiano, S
EP  - 86
N2  - 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.
SN  - 0378-3758
SP  - 70
KW  - Bipartite ranking
KW  -  High dimension and sparsity
KW  -  MCMCPAC-Bayesian aggregation
KW  -  Supervised statistical learning
JF  - Journal of Statistical Planning and Inference
AV  - public
ER  -