学术交流

题目: Greedy Algorithms for Sparse Learning
报告人：Tong Zhang
Department of Statistics
Rutgers University
时间：7月14日，14:00-15:00
地点：南京大学蒙民伟楼404

摘要：Sparse Learning has attracted much attention in recent years. There are two classes of methods: convex relaxation such as L1 regularization and greedy algorithms. Although the former has received more attention in the machine learning community, my opinion is that the latter approach is more flexible and powerful. This talk will discuss variations of greedy algorithms in the context of sparse recovery.

简介: Tong Zhang received a B.A. in mathematics and computer science from Cornell University in 1994 and a Ph.D. in Computer Science from Stanford University in 1998. After graduation, he worked at IBM T.J. Watson Research Center in Yorktown Heights, New York, and Yahoo Research in New York city. He is currently a statistics professor at Rutgers University. His research interests include machine learning, algorithms for statistical computation, their mathematical analysis and applications.

各位同仁：
IEEE CS Nanjing Chapter 5月18日13：30~15：40在南京大学蒙民伟楼404举行学术报告会，信息如下。欢迎参加！

报告一：

题目：Using Computers to Find Out the Truth

报告人：Professor Boi Faltings

ECCAI Fellow, Director of AI Lab

Faculty of Information and Communication Sciences

Swiss Federal Institute of Technology, Lausanne, Switzerland

时间：5月18日 13:30-14:30

地点：蒙民伟楼404会议室

摘要：One of the major problems for decision makers today is that they are far removed from the details that are often crucial for the success of their plans. On the other hand, the people who know these details are often not likely to report them truthfully, as it is not in their best interest to do so. The anonymity afforded by computing systems can help in this situation. I present several approaches to eliciting truthful information, in particular scoring rules, peer prediction methods and opinion polls.

简介：Boi Faltings is a full professor of computer science and director of the AI lab. His main research contributions are in the area of qualitative and case-based reasoning, constraint programming, distributed problem-solving, and recommender systems. He has co-founded 6 companies in e-commerce and computer security and acted as advisor to several other companies world-wide. Prof. Faltings has published over 300 refereed papers and graduated over 25 Ph.D. students, several of which have won national and international awards. Boi Faltings is a fellow of the European Coordinating Committee for Artificial Intelligence. He has served as head of the computer science department from 1996-1998 and as head of the Institute of Core Computing Sciences from 2005-2008. He serves or served as associate editor of several journals, in particular the AI Journal (2000-2008), JAIR (2004-2007), Annals of AI and Mathematics (2008-today), and as member of editorial boards (AI Communications, AI Magazine, Constraints, and others). He also regularly serves in conference committees (IJCAI, AAAI, ECAI, and others) and have been program (co-)chair of several workshops and conferences. He holds a Diploma from ETH Zurich and a Ph.D. from the University of Illinois at Urbana-Champaign.

报告二：

题目：User Experience and Technology Acceptance Issues in Recommender Systems

报告人：Dr. Pearl PU
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各位同仁：

IEEE CS Nanjing Chapter 5月4日16：00~17：00在南京大学蒙民伟楼404举行学术报告会，信息如下。欢迎参加！

Bayesian Ying-Yang System, Best Harmony Learning, and Five Action Circling

LEI XU

Proposed in 1995 and systematically developed over fifteen years, Bayesian Ying-Yang (BYY)  learning is a statistical approach for an intelligent system via two complementary Bayesian representations of a joint distribution on the external observation X and its inner representation R, called BYY system. A Ying-Yang best harmony principle is proposed for learning all the unknowns in the system, in help of an implementation featured by a five action circling.  BYY learning provides not only a general framework that accommodates typical learning approaches from a unified perspective but also a new road that leads to improved model selection criteria, automatic model selection during learning, and coordinated implementation of Ying based model selection and Yang based learning regularization. This talk introduces BYY learning principles, implementing techniques, and typical learning algorithms, in a comparison with other algorithms, particularly with the EM algorithm as a benchmark. These algorithms are summarized in a unified Ying-Yang alternation procedure with major parts in a same expression while differences simply characterized by few options.

Lei Xu, chair professor of Chinese Univ Hong Kong, Chang Jiang Chair Professor of Peking Univ, IEEE Fellow (2001-) and Fellow of International Association for Pattern Recognition (2002-), and Academician of European Academy of Sciences (2002-).  He completed his Ph.D thesis at Tsinghua Univ by the end of 1986, then joined Dept. Math, Peking Univ in 1987 first as a postdoc and then exceptionally promoted to associate professor in 1988 and to a full professor in 1992. During 1989-93 he worked at several universities in Finland, Canada and USA, including Harvard and MIT. He joined CUHK in 1993 as senior lecturer, as  professor in 1996 and chair professor in 2002. He has published a number of well-cited papers on neural networks, statistical learning, and pattern recognition, e.g., his papers got over 3200 citations according to SCI and over 5500 citations according to Google Scholar (GS), with the first 10 papers scored over 2000 (SCI) and 3600 (GS). One single paper has scored 750 (SCI) and 1211 (GS). He served as associate editor for several journals, past governor of international neural network society (INNS), a past president of APNNA, and a member of Fellow committee of IEEE CI Society. Also, he has received several national and international academic awards  (e.g., 1993 National Nature Science Award, 1995 INNS Leadership Award and 2006 APNNA Outstanding Achievement Award).