Online Bayesian Multiple Kernel Bipartite Ranking

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

Collaborative topic regression for online recommender systems: An online and Bayesian approach

National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ

Online Bayesian Passive-Aggressive Learning

We present online Bayesian Passive-Aggressive (BayesPA) learning, a generic online learning framework for hierarchical Bayesian models with max-margin posterior regularization. We provide provable Bayesian regret bounds for both averaging classifiers and Gibbs classifiers. We show that BayesPA subsumes the standard online Passive-Aggressive (PA) learning and more importantly extends naturally to incorporate latent variables for both parametric and nonparametric Bayesian inference, therefore providing great flexibility for explorative analysis. As an important example, we apply BayesPA to topic modeling and derive efficient online learning algorithms for max-margin topic models. We further develop nonparametric BayesPA topic models to resolve the unknown number of topics. Experimental results on 20newsgroups and a large Wikipedia multi-label data set (with 1.1 millions of training documents and 0.9 million of unique terms in the vocabulary) show that our approaches significantly improve time efficiency while maintaining comparable results with the batch counterpart methods. 1