题目：Trace Norm Regularization: Theory, Algorithms, and Applications 报告人：Jieping Ye
Associate Professor
Computer Science and Engineering
Arizona State University 时间：6月23日上午10:30-11:30
地点：404会议室 摘要：
The minimization of a smooth loss function regularized by the trace norm of
the matrix variable has applications in many machine learning tasks including
multi-task learning and matrix completion. The standard semidefinite
programming formulation for this problem is computationally expensive. In this
talk, I will present efficient algorithms for the trace norm regularization
by exploiting the special structure of the trace norm. In addition, I will
discuss the performance bounds of the least squares regression with trace norm
regularization. Finally, I will introduce the SLEP package recently developed
in my group for large-sale sparse learning.
简历：
Jieping Ye is an Associate Professor of the Department of Computer Science
and Engineering at Arizona State University. He received his Ph.D. in Computer
Science from University of Minnesota, Twin Cities in 2005. His research
interests include machine learning, data mining, and biomedical informatics.
He won the outstanding student paper award at ICML in 2004, the SCI Young
Investigator of the Year Award at ASU in 2007, the SCI Researcher of the Year
Award at ASU in 2009, and the NSF CAREER Award in 2010.
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