Online Bayesian Multiple Kernel Bipartite Ranking

Changying Du, Changde Du, Guoping Long, Qing He, Yucheng Li
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, PMLR R14:48-57, 2016.

Abstract

Bipartite ranking aims to maximize the area under the ROC curve (AUC) of a decision function. To tackle this problem when the data appears sequentially, existing online AUC maximization methods focus on seeking a point estimate of the decision function in a linear or predefined single kernel space, and cannot learn effective kernels automatically from the streaming data. In this paper, we first develop a Bayesian multiple kernel bipartite ranking model, which circumvents the kernel selection problem by estimating a posterior distribution over the model weights. To make our model applicable to streaming data, we then present a kernelized online Bayesian passive-aggressive learning framework by maintaining a variational approximation to the posterior based on data augmentation. Furthermore, to efficiently deal with large-scale data, we design a fixed budget strategy which can effectively control online model complexity. Extensive experimental studies confirm the superiority of our Bayesian multi-kernel approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR14-du16a, title = {Online {B}ayesian Multiple Kernel Bipartite Ranking}, author = {Du, Changying and Du, Changde and Long, Guoping and He, Qing and Li, Yucheng}, booktitle = {Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence}, pages = {48--57}, year = {2016}, editor = {Ihler, Alexander and Janzing, Dominik}, volume = {R14}, series = {Proceedings of Machine Learning Research}, month = {25--29 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r14/main/assets/du16a/du16a.pdf}, url = {https://proceedings.mlr.press/r14/du16a.html}, abstract = {Bipartite ranking aims to maximize the area under the ROC curve (AUC) of a decision function. To tackle this problem when the data appears sequentially, existing online AUC maximization methods focus on seeking a point estimate of the decision function in a linear or predefined single kernel space, and cannot learn effective kernels automatically from the streaming data. In this paper, we first develop a Bayesian multiple kernel bipartite ranking model, which circumvents the kernel selection problem by estimating a posterior distribution over the model weights. To make our model applicable to streaming data, we then present a kernelized online Bayesian passive-aggressive learning framework by maintaining a variational approximation to the posterior based on data augmentation. Furthermore, to efficiently deal with large-scale data, we design a fixed budget strategy which can effectively control online model complexity. Extensive experimental studies confirm the superiority of our Bayesian multi-kernel approach.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Online Bayesian Multiple Kernel Bipartite Ranking %A Changying Du %A Changde Du %A Guoping Long %A Qing He %A Yucheng Li %B Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2016 %E Alexander Ihler %E Dominik Janzing %F pmlr-vR14-du16a %I PMLR %P 48--57 %U https://proceedings.mlr.press/r14/du16a.html %V R14 %X Bipartite ranking aims to maximize the area under the ROC curve (AUC) of a decision function. To tackle this problem when the data appears sequentially, existing online AUC maximization methods focus on seeking a point estimate of the decision function in a linear or predefined single kernel space, and cannot learn effective kernels automatically from the streaming data. In this paper, we first develop a Bayesian multiple kernel bipartite ranking model, which circumvents the kernel selection problem by estimating a posterior distribution over the model weights. To make our model applicable to streaming data, we then present a kernelized online Bayesian passive-aggressive learning framework by maintaining a variational approximation to the posterior based on data augmentation. Furthermore, to efficiently deal with large-scale data, we design a fixed budget strategy which can effectively control online model complexity. Extensive experimental studies confirm the superiority of our Bayesian multi-kernel approach. %Z Reissued by PMLR on 04 October 2026.
APA
Du, C., Du, C., Long, G., He, Q. & Li, Y.. (2016). Online Bayesian Multiple Kernel Bipartite Ranking. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R14:48-57 Available from https://proceedings.mlr.press/r14/du16a.html. Reissued by PMLR on 04 October 2026.

Related Material