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Workshop
The Generative and Discriminative Learning Interface
Simon Lacoste-Julien · Percy Liang · Guillaume Bouchard

Sat Dec 12 07:30 AM -- 07:00 PM (PST) @ Westin: Alpine DE
Event URL: http://gen-disc2009.wikidot.com/call »

Generative and discriminative learning are two of the major paradigms for solving prediction problems in machine learning, each offering important distinct advantages. They have often been studied in different sub-communities, but over the past decade, there has been increasing interest in trying to understand and leverage the advantages of both approaches. The goal of this workshop is to map out our current understanding of the empirical and theoretical advantages of each approach as well as their combination, and to identify open research directions.

Author Information

Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal)
Percy Liang (Stanford University)
Percy Liang

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).

Guillaume Bouchard (Xerox Research Center Europe)

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