IC Colloquium : Learning in networks: How to exploit relationships to improve predictions
Event details
| Date | 28.11.2017 |
| Hour | 16:15 › 17:30 |
| Location | |
| Category | Conferences - Seminars |
By : Jennifer Neville - Purdue University
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, heterogeneous, and dynamic networks, and describe methods for semi-supervised learning and latent-variable embeddings to address the challenges.
Bio :
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. She is currently an elected member of the AAAI Executive Council and she was recently 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 work, which includes more than 100 peer-reviewed publications with over 5000 citations, focuses on developing data mining and machine learning techniques for complex relational and network domains, including social, information, and physical networks.
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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, heterogeneous, and dynamic networks, and describe methods for semi-supervised learning and latent-variable embeddings to address the challenges.
Bio :
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. She is currently an elected member of the AAAI Executive Council and she was recently 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 work, which includes more than 100 peer-reviewed publications with over 5000 citations, focuses on developing data mining and machine learning techniques for complex relational and network domains, including social, information, and physical networks.
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Practical information
- General public
- Free
- This event is internal
Contact
- Host : Robert West