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Learning Structural SVMs with Latent Variables Chun-Nam John Yu cnyu@cs.cornell.edu Thorsten Joachims tj@cs.cornell.edu Department of Computer Science, Cornell University, Ithaca, NY 14850 USA Abstract We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of applica- tion problems, with an opt
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Group Lasso with Overlap and Graph Lasso Laurent Jacob LAURENT.JACOB@MINES-PARISTECH.FR Mines ParisTech – CBIO, INSERM U900, Institut Curie, 26 rue d’Ulm, Paris cedex 05, F-75248 France Guillaume Obozinski1 GOBO@STAT.BERKELEY.EDU INRIA – Willow Pro
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Good Learners for Evil Teachers Ofer Dekel OFERD@MICROSOFT.COM Microsoft Research, 1 Microsoft Way, Redmond, WA 98052, USA Ohad Shamir OHADSH@CS.HUJI.AC.IL The Hebrew University, Jerusalem 91904, Israel Abstract We consider a supervised machine learning
