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Computer Science Department Colloquium
2/21/2014 02:00 pm
CoRE A(Room 301)

Learning with Asymmetric Information

Christoph H. Lampert, Institute of Science and Technology, Austria

Faculty Host: Dimitris Metaxas

Abstract

Many computer vision problems have an asymmetric distribution of information, i.e. less or more information about a problem is available at training time than at test time. In my talk I will discuss our recent work on both situations: 1) the LUPI framework for the case when we have additional data modalities available for the training data, and 2) a label propagation approach for the case when an additional similarity measure is available at test time (both published at ICCV 2013).

Bio

Christoph Lampert received the PhD degree in mathematics from the University of Bonn in 2003. Afterwards, he held postdoctoral positions at the German Research Center for Artificial Intelligence in Kaiserslautern and the Max Planck Institute for Biological Cybernetics in Tübingen. Since 2010 he is an assistant professor at the Institute of Science and Technology Austria (IST Austria), where he heads a research group for computer vision and machine learning. Dr Lampert received several international and national awards for his research, including the best paper prize of CVPR 2008 and best student paper award of ECCV 2008. In 2012 he was awarded an ERC Starting Grant by the European Research Council. He is an Associate Editor of the IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI) and Action Editor for the Journal of Machine Learning Research (JMLR).