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.


  • David B. Dunson, Duke University
  • 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 at Berkeley
  • Peter Orbanz, Cambridge University
  • Yee Whye Teh, University College London
  • Larry Wasserman, Carnegie Mellon University