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SUMMARY:IC Colloquium : Learning in networks: How to exploit relationships
  to improve predictions
DTSTART:20171128T161500
DTEND:20171128T173000
DTSTAMP:20260410T010034Z
UID:f565bdf36c67e6b419131e0aee994899ae5cc09ac5866b1d0b086062
CATEGORIES:Conferences - Seminars
DESCRIPTION:By : Jennifer Neville - Purdue University\n\nAbstract :\nThe p
 opularity of social networks and social media has increased the amount of 
 information available about users' behavior online--including current acti
 vities\, and interactions among friends and family. This rich relational i
 nformation can be used to improve predictions even when individual data is
  sparse\, since the characteristics of friends are often correlated. Altho
 ugh this type of network data offer several opportunities to improve predi
 ctions about users\, the characteristics of online social network data als
 o present a number of challenges to accurately incorporate the network inf
 ormation into machine learning systems. This talk will outline some of the
  algorithmic and statistical challenges that arise due to partially-observ
 ed\, heterogeneous\, and dynamic networks\, and describe methods for semi-
 supervised learning and latent-variable embeddings to address the challeng
 es.\n\nBio :\nJennifer Neville is the Miller Family Chair Associate Profes
 sor of Computer Science and Statistics at Purdue University. She received 
 her PhD from the University of Massachusetts Amherst in 2006. She is curre
 ntly 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. I
 n 2012\, she was awarded an NSF Career Award\, in 2008 she was chosen by I
 EEE 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 th
 an 100 peer-reviewed publications with over 5000 citations\, focuses on de
 veloping data mining and machine learning techniques for complex relationa
 l and network domains\, including social\, information\, and physical netw
 orks.\n\nMore information
LOCATION:BC 410 https://plan.epfl.ch/?room==BC%20410
STATUS:CONFIRMED
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