南京大学计算机软件新技术国家重点实验室   

摘要：

In the past decade, we have seen dramatic evolution of machine learning from convex methods to non-convex methods and from shallow models to deep models. Lying at the heart of machine learning, mathematical optimization has played an important and indispensable role in solving many different learning problems. However, there is still a big gap between the practice used in deep learning community and the existing theory. In this talk, I will focus on a learning paradigm called stagewise learning that is different from conventional learning methods based on stochastic gradient descent with a continuously decreasing step size. I will show that the proposed stagewise learning algorithms can achieve significant improvements in both theory and practice over standard stochastic gradient method for solving many machine learning problems, including support vector machine, AUC optimization, deep neural networks and generative adversarial networks.

报告人简介:

Dr. Tianbao Yang is currently an assistant professor at the University of Iowa. He received his Ph.D. degree in Computer Science from Michigan State University in 2012. Before joining UIowa, he was a researcher in NEC Laboratories America at Cupertino (2013-2014) and a Machine Learning Researcher in GE Global Research (2012-2013), mainly focusing on developing deep learning algorithms and distributed optimization systems for machine learning applications. Dr. Yang has board interests in machine learning and he has focused on several research topics, including online learning, distributed optimization, stochastic optimization, deep learning, and learning theory. His recent research interests revolve around designing fast optimization algorithms for non-convex problems including deep learning, GAN, etc. He has published over 80 papers in top-notch venues. He has won multiple awards including NSF Career Award, UIowa Dean’s Excellence Scholar Award, the Best student paper award at 25th Conference on Learning Theory (COLT). He is an associate editor of the Neurocomputing Journal.

时间：5月16日（星期四）14:00

地点：计算机科学技术楼230室