CALL FOR CONTRIBUTIONS ------------------------------------------------------------------------- NIPS 2011 Workshop Bayesian Nonparametrics: Hope or Hype? http://people.seas.harvard.edu/~rpa/nips2011npbayes.html http://nips.cc/
December 16 or 17, 2011 Sierra Nevada, Spain ------------------------------------------------------------------------- We invite submissions on Bayesian nonparametric methods, specifically seeking empirical and/or theoretical results that shed light on the successes and failures of these methods for practical problems. We particularly welcome contributions which examine the tradeoffs between parametric and nonparametric models, and frequentist vs. Bayesian variants thereof. Please submit PDF abstracts of up to two pages in the standard NIPS format to nips2011npba...@cs.toronto.edu by October 21, 2011. Acceptance decisions will be emailed in time for meeting the NIPS early registration deadline in early November. Organizers: Ryan Prescott Adams, Harvard University Emily B. Fox, University of Pennsylvania Advisory Panel: David B. Dunson, Duke University Zoubin Ghahramani, University of Cambridge Michael I. Jordan, University of California, Berkeley Peter Orbanz, University of Cambridge Yee Whye Teh, University College London Larry Wasserman, Carnegie Mellon University Workshop Description: Bayesian nonparametric methods are an expanding part of the machine learning landscape. Proponents of Bayesian nonparametrics claim that these methods enable one to construct models that can scale their complexity with data, while representing uncertainty in both the parameters and the structure. Detractors point out that the characteristics of the models are often not well understood and that inference can be unwieldy. Relative to the statistics community, machine learning practitioners of Bayesian nonparametrics frequently do not leverage the representation of uncertainty that is inherent in the Bayesian framework. Neither do they perform the kind of analysis --- both empirical and theoretical --- to set skeptics at ease. In this workshop we hope to bring a wide group together to constructively discuss and address these goals and shortcomings. _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
