The Machine Learning Series provides a forum for presentation and discussion of interesting and current machine learning issues. The talks that are scheduled for 2010 will be listed below.
Unless otherwise noted, all talks will be in room DC 1302. Coffee to be confirmed
Presentation slides will be posted whenever possible. Please click on the presentation title to access these notes (usually in pdf format).
Machine Learning Seminar Series is supported by
2010 Seminars - Distinguished Speakers
|Wednesday, May 5, 2010, 4:00 p.m., MC 5158 webcast|
|Title:||Frequents vs. Bayesians, the PAC-Bayesian synthesis, and support vector machines.|
|Abstract:||We will start with a description of the frequentist (objective probability) and Bayesian (subjective probability) positions. We will then describe the PAC-Bayesian theorem which allows for a kind of formal synthesis of the two positions. The talk will then focus on support vector machines as a case study in PAC-Bayesian analysis. We will discuss the "SVM scandal" --- no meaningful formal justification for the hinge loss of soft SVMs has ever been given. We will also apply PAC-Bayesian analysis to recent trends in structural SVMs. Structural SVMs are a way of training the parameters of graphical models and are becoming increasingly popular in areas such as computer vision and natural language processing.|
|Bio:||Professor McAllester received his B.S., M.S., and Ph.D. degrees from the Massachusetts Institute of Technology in 1978, 1979, and 1987 respectively. He served on the faculty of Cornell University for the academic year of 1987-1988 and served on the faculty of MIT from 1988 to 1995. He was a member of technical staff at AT&T Labs-Research from 1995 to 2002. He has been a fellow of the American Association of Artificial Intelligence (AAAI) since 1997. Since 2002 he has been Chief Academic Officer at the Toyota Technological Institute at Chicago. He has authored over 90 refereed publications. Professor McAllester's research areas include machine learning, the theory of programming languages, automated reasoning, AI planning, computer game playing (computer chess), computational linguistics and computer vision. A 1991 paper on AI planning proved to be one of the most influential papers of the decade in that area. A 1993 paper on computer game algorithms influenced the design of the algorithms used in the Deep Blue system that defeated Gary Kasparov. A 1998 paper on machine learning theory introduced PAC-Bayesian theorems which combine Bayesian and nonBayesian methods. He is currently part of a team that has scored in the top two places in the PASCAL object detection challenge (computer vision) in 2007, 2008 and 2009.|
|Wednesday, July 14, 2010 2:15 p.m., DC 1304 webcast|
|Title:||Machine Learning in the Data Revolution Era|
|Abstract:||Machine learning is playing a central role in the digital revolution, in which massive and never-ending data is collected from various sources such as online commerce, social networking, and online collaboration. This large amount of data is often noisy or partial. In this talk I will present learning algorithms appropriate for this new era: algorithms that not only can handle massive amounts of data but can also leverage large data sets to reduce the required runtime; and algorithms that can use the multitude of examples to compensate for lack of full information on each individual example.|
|Bio:||Shai Shalev-Shwartz is on the faculty of the Department of Computer Science and Engineering at the Hebrew university of Jerusalem, Israel. Dr. Shalev-Shwartz received the PhD degree in computer science from the Hebrew university, in 2007. Between 2007-2009 he was a research assistant professor at Toyota Technological Institute at Chicago. Shai has written more than 40 research papers, focusing on learning theory, online prediction, optimization techniques, and practical algorithms. He served as a program committee member for the COLT conference in 2008-2010, a program committee member for ALT in 2009, and he is part of the editorial boards of the Journal of Machine Learning Research (JMLR) and the Machine Learning Journal (MLJ).|
|Date TBA, Time TBA|