Yiming Ying, University at Albany

Abstract

Stochastic optimization algorithms such as stochastic gradient descent (SGD) update the model sequentially with cheap per-iteration costs, making them amenable for large-scale streaming data analysis. However, most of the existing studies focus on the classification accuracy which can not be directly applied to the important problems of maximizing the Area under the ROC curve (AUC) in imbalanced classification and bipartite ranking. In this talk, I will talk about our recent work on developing novel stochastic optimization algorithms for AUC maximization (aka bipartite ranking). Compared with the previous literature which requires high storage and per-iteration costs, our algorithms have both space and per-iteration costs of one datum and can achieve optimal convergence rates.