About
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.
Workshop homepage: http://people.seas.harvard.edu/~rpa/nips2011npbayes.html
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Uploaded videos:
Why Bayesian nonparametrics?
Jan 24, 2012
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23617 Views
Invited Talks
Spatial Bayesian Nonparametrics for Natural Image Segmentation
Jan 24, 2012
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5574 Views
Discussion of Erik Sudderth's talk: NPB Hype or Hope?
Jan 24, 2012
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11196 Views
Two tales about Bayesian nonparametric modeling
Jan 24, 2012
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6905 Views
Discussion of Igor Pruenster´s talk
Jan 24, 2012
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4610 Views
Scaling Latent Variable Models
Jan 24, 2012
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5922 Views
Discussion of Alex Smola's talk: Remarks on parallelised MCMC
Jan 24, 2012
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6310 Views
What to do about M-open? A decision theoretic (distribution free) solution
Jan 24, 2012
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3483 Views
Discussion of Christopher Holmes's talk: What to do about M-open?
Jan 31, 2012
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5025 Views