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Representation, Inference and Learning in Structured Statistical Models
Lise Getoor

Mon Dec 03 09:30 AM -- 11:30 AM (PST) @ Emerald Bay B, Harveys Convention Center Floor (CC)

Addressing inherent uncertainty and exploiting structure are fundamental to understanding, designing and making predictions in large-scale information, biological and socio-technical systems. Statistical relational learning (SRL) builds on principles from probability theory and statistics to address uncertainty while incorporating tools from logic to represent structure. SRL methods are especially well-suited to domains where the input is best described as a large multi-relational network, such as online social media and communication networks, and we need to make structured predictions.

The first part of the tutorial will provide an introduction to key SRL concepts, including relational feature construction and representation, inference and learning methods for "lifted graphical models." The second part of the tutorial will describe three important challenges in network analysis: graph identification (inferring a graph from noisy observations), graph alignment (mapping components in one graph to another) and graph summarization (clustering the nodes and edges in a graph). I will overview approaches to these problems based on SRL methods, describe available datasets, and highlight opportunities for future research.

Throughout, I will pay particular attention to scaling and make connections to related areas of machine learning such as structured prediction and latent factor models.

Author Information

Lise Getoor (UC Santa Cruz)

Lise Getoor is an Associate Professor in the Computer Science Department and the Institute for Advanced Computer Studies at the University of Maryland, College Park. Her research areas include machine learning, reasoning under uncertainty, and database management. She is co-editor with Ben Taskar of the book 'An Introduction to Statistical Relational Learning', MIT Press, 2007. She is a board member of the International Machine Learning Society, and has served as Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, JAIR Associate Editor, and on the AAAI Council. She is a recipient of several best paper awards, an NSF Career Award and a National Physical Sciences Consortium Fellowship. She received her PhD from Stanford University, her Master’s degree from the University of California, Berkeley, and her undergraduate degree from the University of California, Santa Barbara.

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