Workshop at the 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009)
Abstract submission deadline: October 25, 2009
Notification to authors: November 5, 2009 (NIPS Early registration deadline is November 6)
Final version: November 27, 2009
Workshop: December 12, 2009
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.
|The workshop is sponsored by the PASCAL-2 European Network of Excellence
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 and 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
- Michael Collins, MIT
- Dan Klein, UC Berkeley
- Tony Jebara, Columbia University
- Ben Taskar, University of Pennsylvania
- John Winn, Microsoft Research Cambridge
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.
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 here (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 the CMT website to submit your paper (you'll need to create a login first). DEADLINE: October 25, 2009, 11:30 PDT
Westin Resort and Spa / Hilton Whistler Resort and Spa
Whistler, B.C., Canada
Saturday December 12, 2009
- Simon Lacoste-Julien (University of Cambridge)
- Percy Liang (UC Berkeley)
- Guillaume Bouchard (Xerox Research Centre Europe)
CONTACT: gen.disc.nips09 at gmail.com