Tutorials
Title: Partially Observable Markov Decision Process
Abstract:
Partially Observable Markov Decision Process (POMDP) provides a mathematically elegant formulation for adapting the actions of an agent based on past observations in order to achieve high expected rewards in the future. However, solving POMDPs is computationally intractable in the worst case, and until recently POMDPs were considered to be impractical for applications. In the last few years, tremendous progress has been made in solving POMDPs and they have been shown to be effective in application domains such as dialog systems, assistive technologies for the elderly, and aircraft collision avoidance systems. In this tutorial, we will go through the basic properties of POMDPs, try to understand when they are likely to be effectively solvable, and describe techniques for scaling to problems with very large state spaces and long search horizons.
Short biography:
Wee Sun Lee is an associate professor in the Department of Computer Science at the National University of Singapore. He obtained his PhD from the Australian National University in 1996 and was a research fellow at the Australian Defence Force Academy from 1996 to 1998 prior to joining the National University of Singapore.
He is interested in machines that learn, perform inference, make decisions and plan. He works on obtaining theoretical understanding of when learning, inference and planning can be done effectively, on developing effective algorithms for these problems, and also on applying the algorithms to applications such as information extraction, natural language understanding, robotics and games.
Title: Learning from Graph Data: Graph Kernels, Graph Mining and Recent Developments
Abstract:
Labeled Graphs are general and powerful data structures that can be used to represent diverse kinds of objects such as XMLs, chemical compounds, proteins, and RNAs. In these 10 years, we saw significant progress in statistical learning algorithms for graph data, such as supervised classification, clustering and dimensionality reduction.
Graph kernels and graph mining have been the main driving force of such innovation. In this tutorial, I start from basics of the two techniques and cover several important algorithms in learning from graphs. Successful biological applications are featured. If time allows, I also cover recent developments and show future directions
Short biography:
Koji Tsuda is Senior Research Scientist at AIST Computational Biology Research Center. He is also affiliated with ERATO Minato Project, Japan Science and Technology Agency (JST). After completing his Dr.Eng. in Kyoto University in 1998, he joined former Electrotechnical Laboratory (ETL), Tsukuba, Japan, as Research Scientist. When ETL is reorganized as AIST in 2001, he joined newly established Computational Biology Research Center, Tokyo, Japan. In 2000-2001, he worked at GMD FIRST (current Fraunhofer FIRST) in Berlin, Germany, as Visiting Scientist. In 2003-2004 and 2006-2008, he worked at Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, first as Research Scientist and later as Project Leader. He has published more than 70 papers in refereed conferences and journals, and served as an area chair and a program committee member in leading machine learning conferences such as NIPS and ICML. IPSJ Nagao Award (2009).
Professor of Computer Science & Engineering
University of Washington
Title: Open Information Extraction at Web Scale
Abstract:
Information Extraction (IE) is the task of mapping natural-language sentences to machine-processable representations of the sentences' content. IE is often formulated as a machine-learning problem where an extractor for a particular relation (e.g., seminar speaker) is learned from labeled training examples. My talk will describe Open IE---an approach to scaling IE to the Web where the set of potential relations is not known in advance making a standard machine learning approach impossible. I will describe various bootstrapping approaches that enables us to utilize machine learning at Web scale.
Short biography:
Oren Etzioni is the Washington Research Foundation Entrepreneurship Professor at the University of Washington's Computer Science Department. He received his bachelor's degree in Computer Science from Harvard University in June 1986 where he was the first Harvard student to "major" in Computer Science. Etzioni received his Ph.D. from Carnegie Mellon University in January 1991, and joined the University of Washington's faculty in February 1991, where he is now a Professor of Computer Science. Etzioni received a National Young Investigator Award in 1993, and was selected as a AAAI Fellow a decade later. In 2007, he received the Robert S. Engelmore Memorial Award. He is the founder and director of the University of Washington's Turing Center.
Etzioni is the author of over 100 technical papers in a wide range of conferences including AAAI, ACL, CIDR, COLING, EMNLP, FOCS, HLT, ICML, IJCAI, ISWC, IUI, KDD, KR, SIGIR, and WWW. He is a founder of three companies and a Venture Partner at the Madrona Venture Group. His work has been featured in the New York Times, Wall Street Journal, NPR, SCIENCE, The Economist, TIME Magazine, Business Week, Newsweek, Discover Magazine, Forbes Magazine, Wired, NBC Nightly News, and even Pravda.