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What and Who

Learning in networks: How to exploit relationships to improve predictions

Prof. Jennifer Neville
Purdue University
MPI Colloquium Series Distinguished Speaker

Jennifer Neville is the Miller Family Chair Associate Professor of Computer Science and Statistics at Purdue University. She received her PhD from the University of Massachusetts Amherst in 2006. In 2016, she was PC chair of the 9th ACM International Conference on Web Search and Data. In 2012, she was awarded an NSF Career Award, in 2008 she was chosen by IEEE as one of "AI's 10 to watch", and in 2007 was selected as a member of the DARPA Computer Science Study Group. Her research focuses on developing data mining and machine learning techniques for relational domains, which include social, information, and physical networks.
https://www.cs.purdue.edu/homes/neville/
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI  
Public Audience
English

Date, Time and Location

Thursday, 10 March 2016
11:00
120 Minutes
E1 4
024
Saarbrücken

Abstract

The popularity of social networks and social media has increased the amount of information available about users' behavior online--including current activities, and interactions among friends and family. This rich relational information can be used to improve predictions even when individual data is sparse, since the characteristics of friends are often correlated. Although this type of network data offer several opportunities to improve predictions about users, the characteristics of online social network data also present a number of challenges to accurately incorporate the network information into machine learning systems. This talk will outline some of the algorithmic and statistical challenges that arise due to partially-observed, large-scale networks, and describe methods for semi-supervised learning, latent-variable modeling, and active sampling to address the challenges.

Contact

Petra Schaaf
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Petra Schaaf, 03/07/2016 11:32
Petra Schaaf, 03/07/2016 11:31 -- Created document.