题目：
Active Learning for Regression: Algorithms and Applications
报告人：
Masashi Sugiyama
Department of Computer Science
Tokyo Institute of Technology, Tokyo, Japan
时间：
11月5日（星期四） 下午4:30-5:30
地点：
蒙民伟楼404会议室
摘要：
Active learning is a challenging task in supervised learning: users are allows
to choose input points (questions) to gather output values (answers) for
better generalization. This has some analogy to human learning that asking
good questions to the teacher boosts the speed of learning. Traditional
active learning methods for regression assume that the model at hand is
correctly specified. However, this assumption is rarely satisfied in practice
and the traditional methods are not reliable without this assumption. In
this talk, I introduce a recently proposed active learning method for
regression. The method is shown to be valid also for misspecified models,
while algorithmic simplicity is kept moderately. I also show successful real-
world examples of active learning such as semi-conductor wafer alignment and
robot control.
简历：
Masashi Sugiyama received B.E., M.E., and Ph.D. degrees in Computer Science
from Tokyo Institute of Technology, Japan in 1997, 1999, and 2001,
respectively. In 2001, he was appointed as an Assistant Professor in the same
institute and he has been an Associate Professor since 2003. His research
interests include theory and application of machine learning, robot control,
and optical measurement. From 2003 to 2004, he was an Alexander von Humboldt
Research Fellow and stayed at Fraunhofer Institute FIRST.IDA, Berlin, Germany.
In 2007, he received Faculty Award from IBM for his contribution to non-
stationarity adaptation in machine learning. A part of the achievements was
included in his co-editted book Dataset Shift in Machine Learning published
from the MIT Press in 2009.
