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Nonparametric learning from Bayesian models with randomized objective functions
Simon Lyddon · Stephen Walker · Chris C Holmes

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #24

Bayesian learning is built on an assumption that the model space contains a true reflection of the data generating mechanism. This assumption is problematic, particularly in complex data environments. Here we present a Bayesian nonparametric approach to learning that makes use of statistical models, but does not assume that the model is true. Our approach has provably better properties than using a parametric model and admits a Monte Carlo sampling scheme that can afford massive scalability on modern computer architectures. The model-based aspect of learning is particularly attractive for regularizing nonparametric inference when the sample size is small, and also for correcting approximate approaches such as variational Bayes (VB). We demonstrate the approach on a number of examples including VB classifiers and Bayesian random forests.

Author Information

Simon Lyddon (University of Oxford)
Stephen Walker (University of Texas at Austin)
Chris C Holmes (University of Oxford)

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