An adaptive gradient method for online AUC maximization

• Main Authors: DING, Yi, ZHAO, Peilin, HOI, Steven C. H., ONG, Yew-Soon
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Adaptive algorithms; Adaptive gradient methods; Benchmark datasets; Effectiveness and efficiencies; Empirical performance; Nonadaptive algorithm; Simple stochastic; Theoretical bounds; Updating strategy; Computer Sciences; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/2638; https://ink.library.smu.edu.sg/context/sis_research/article/3638/viewcontent/AdaptiveGradientAUCmax_2015_aaai.pdf
• Institution: Singapore Management University

Learning for maximizing AUC performance is an important research problem in machine learning. Unlike traditional batch learning methods for maximizing AUC which often suffer from poor scalability, recent years have witnessed some emerging studies that attempt to maximize AUC by single-pass online le...