Events Calendar

CS Distinguished Lecture: Lise Getoor (University of Maryland College Park): Statistical Relational Learning and Graph Identification

Speaker: Lise Getoor, University of Maryland College Park

Talk Title: Statistical Relational Learning and Graph Identification

Series: CS Distinguished Lectures

Abstract: Within the machine learning and data mining communities, 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). In this talk, I’ll give a brief overview of SRL, discuss its relation to graph analysis, extraction, and alignment, and its importance in the context of big data analytics. I’ll then describe our recent work on "graph identification", the process of inferring a graph or network from observational data. Graph identification requires a combination of entity resolution (determining when two references refer to the same underlying entity), link prediction (inferring missing relationships in the data), and collective classification (inferring attribute values of the entities). This form of structured prediction allows us to infer missing information and correct mistakes -- a vital first step before further network analysis is performed. 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. These methods support emerging information extraction and database techniques to realize the promise of extracting actionable knowledge from large-scale data in the wild. I will conclude by highlighting connections to privacy in social network data and other big data challenges.

Biography: 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.

Host: Leana Golubchik

Location: Seaver Science Library (SSL) - 150

Audiences: Everyone Is Invited

Contact: Assistant to CS chair