Home > Seminars > IBM Lecture Series - Statistical Relational Learning and Graph Identification

IBM Lecture Series - Statistical Relational Learning and Graph Identification

Start:

11/15/2012 at 3:30PM

End:

11/15/2012 at 5:00PM

Location:

356A Fitzpatrick

Host:

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Nitesh Chawla

Nitesh Chawla

VIEW FULL PROFILE Email: nchawla@nd.edu
Phone: 574-631-1090
Website: http://www.nd.edu/~nchawla/
Office: 384 Nieuwland Science Hall

Affiliations

College of Engineering Frank M. Freimann Professor
Dr. Chawla's research interests are broadly in the areas of Big Data: data science, machine learning, network science and their applications social networks, healthcare informatics/analytics, and climate/environmental sciences. He directs the Notre Dame Interdisciplinary Center for Network ...
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Within the machine learning community, there is a growing interest in learning structured models from input data that is itself structured, an area often referred to as statistical relational learning (SRL). I’ll begin with a brief overview of SRL, and discuss its relation to network analysis, extraction, and alignment. I’ll then describe our recent work on graph identification. Graph identification is the process of transforming an observed input network into an inferred output graph. It involves cleaning the data -- inferring missing information and correcting mistakes – and is an important first step before any further network analysis is performed. It requires a combination of entity resolution, link prediction, and collective classification techniques. I will overview two approaches to graph identification: 1) coupled conditional classifiers (C^3), and 2) probabilistic soft logic (PSL). I will describe their mathematical foundations, learning and inference algorithms, and empirical evaluation, showing their power in terms of both accuracy and scalability. I will conclude by highlighting connections to privacy in social network data and other current big data challenges.

Seminar Speaker:

Lise Getoor

Associate Professor of Computer Science at the University of Maryland at College Park

Lise Getoor is an Associate Professor in the Computer Science Department at the University of Maryland, College Park and University of Maryland Institute for Advanced Computer Studies. Her research areas include machine learning, and reasoning under uncertainty; in addition she works in data management, visual analytics and social network analysis. She is a board member of the International Machine Learning Society, a former Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, JAIR Associate Editor, and she has served on the AAAI Council. She was conference co-chair for ICML 2011, and has served on the PC of many conferences including the senior PC for AAAI, ICML, KDD, UAI and the PC of SIGMOD, VLDB, and WWW. She is a recipient of an NSF Career Award and was awarded a National Physical Sciences Consortium Fellowship. Her work has been funded by ARO, DARPA, IARPA, Google, IBM, LLNL, Microsoft, NGA, NSF, Yahoo! and others. She received her PhD from Stanford University, her Master’s degree from University of California, Berkeley, and her undergraduate degree from University of California, Santa Barbara.

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