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************************************************************************ CALL FOR PAPERS - The Generative and Discriminative Learning Interface Workshop at the 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009) December 12, 2009, Whistler, Canada http://gen-disc2009.wikidot.com Submission Deadline: October 25, 2009 ************************************************************************ OVERVIEW 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. BACKGROUND AND OBJECTIVES In generative approaches for prediction tasks, one models a joint distribution on inputs and outputs and parameters are typically estimated using a likelihood-based criterion. In discriminative approaches, one directly models the mapping from inputs to outputs (either as a conditional distribution or simply as a prediction function); parameters are estimated by optimizing various objectives related to a loss function. Discriminative approaches have shown better performance with enough data, as they are better tuned to the prediction task and are more robust to model misspecification. Despite the strong empirical success of discriminative methods in a wide range of applications, when the structures to be learned become complex (e.g. in machine translation, scene understanding, biological process discovery), even large training sets become sparse relative to the task, and this sparsity can only be mitigated if some other source of information comes into play to constrain the space of fitted models, such as unlabeled examples, related data sources or human prior knowledge about the problem. Generative modeling is a principle way of encoding this additional information, e.g. through probabilistic graphical models or stochastic grammar rules. Moreover, they provide a natural way to make use of unlabeled data and can be more computationally efficient for some models. See http://gen-disc2009.wikidot.com/call for a more detailed background with references. The aim of this workshop is to provide a platform for both theoretical and applied researchers from different communities to discuss the status of our understanding on the interplay between generative and discriminative learning, as well as to identify forward-looking open problems of interest to the NIPS community. Examples of topics of interest to the workshop are as follows: * Theoretical analysis of generative vs. discriminative learning * Techniques for combining generative / discriminative approaches * Successful applications of hybrids * Empirical comparison of generative vs. discriminative learning * Inclusion of prior knowledge in discriminative methods (semi-supervised approaches, generalized expectation criteria, posterior regularization, etc.) * Insights into the role of generative / discriminative interface for deep learning * Computational issues in discriminatively trained generative models/hybrid models * Map of possible generative / discriminative approaches and combinations * Bayesian approaches optimized for predictive performance * Comparison of model-free and model-based approaches in statistics or reinforcement learning INVITED SPEAKERS / PANELISTS Dan Klein, UC Berkeley http://www.cs.berkeley.edu/~klein/ Tony Jebara, Columbia University http://www1.cs.columbia.edu/~jebara/ Ben Taskar, University of Pennsylvania http://www.seas.upenn.edu/~taskar/ John Winn, Microsoft Research Cambridge http://johnwinn.org/ IMPORTANT DATES Deadline for abstract submission: October 25, 2009 Notification of acceptance: November 5, 2009 (NIPS early registration deadline is November 6) Final version: November 20, 2009 Workshop: December 12, 2009 LOCATION Westin Resort and Spa / Hilton Whistler Resort and Spa Whistler, B.C., Canada http://nips.cc/Conferences/2009/ CALL FOR PARTICIPATION Researchers interested in presenting their work and ideas on the above themes are invited to submit an extended abstract of 2-4 pages in pdf format using the NIPS style available at http://nips.cc/PaperInformation/StyleFiles (author names don't need to be anonymized). Submissions will be accepted either as contributed talks or poster presentations, and we expect the speakers to provide a final version of their paper by November 20 to be posted on the workshop website. Sign on at: https://cmt.research.microsoft.com/GDLI2009 to submit your paper (you'll need to create a login first). WORKSHOP FORMAT This 1 day workshop will have a mix of invited talks (3), contributed talks (4-8), a poster session as well as a panel discussion. We will leave plenty of time and encourage discussion throughout the day. We also encourage the participants to visit the online forum in December to discuss the submitted papers and the themes of the workshop. ORGANIZERS Simon Lacoste-Julien (University of Cambridge) Percy Liang (UC Berkeley) Guillaume Bouchard (Xerox Research Centre Europe) CONTACT gen.disc.nips09 at gmail.com -- Simon Lacoste-Julien Postdoctoral Research Associate Machine Learning Group University of Cambridge http://mlg.eng.cam.ac.uk/slacoste/ _______________________________________________ uai mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/uai
