Tutorials

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Wee Sun Lee


Associate Professor
Department of Computer Science
National University of Singapore

http://www.comp.nus.edu.sg/~leews
Title: Partially Observable Markov Decision Process Slides
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.


Bio: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.





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Koji Tsuda


Senior Research Scientist
Leader, Machine Learning Research Group
Computational Biology Research Center
National Institute of Advanced Industrial Science and Technology

http://www.cbrc.jp/~tsuda
Title: Learning from Graph Data: Graph Kernels, Graph Mining and Recent Developments Slides
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


Bio:
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. He received IPSJ Nagao Award in 2009.