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Predictive adaptation of hybrid Monte Carlo with bandits

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dc.contributor.author Wang, Ziyu
dc.date.accessioned 2012-10-10T19:14:31Z
dc.date.available 2012-10-10T19:14:31Z
dc.date.copyright 2012 en_US
dc.date.issued 2012-10-10
dc.identifier.uri http://hdl.handle.net/2429/43362
dc.description.abstract This thesis introduces a novel way of adapting the Hybrid Monte Carlo (HMC) algorithm using Gaussian process bandits. HMC is a powerful Markov chain Monte Carlo (MCMC) method, but it requires careful tuning of its hyper-parameters. We propose a Gaussian process bandit approach to carry out the adaptation of the hyper-parameters while the Markov chain progresses. We also introduce the use of cross-validation error measures for adaptation, which we believe are more pragmatic than many existing adaptation objectives. The new measures take the intended statistical use of the model, whose parameters are estimated by HMC, into consideration. We apply these two innovations to the adaptation of HMC for prediction and feature selection with multi-layer feed-forward neural networks. The experiments with synthetic and real data show that the proposed adaptive scheme is not only automatic, but also does better tuning than human experts. en_US
dc.language.iso eng en_US
dc.publisher University of British Columbia en
dc.title Predictive adaptation of hybrid Monte Carlo with bandits en_US
dc.type Electronic Thesis or Dissertation en
dc.degree.name Master of Science - MSc en_US
dc.degree.discipline Computer Science en_US
dc.degree.grantor University of British Columbia en
dc.date.graduation 2012-11 en_US
dc.degree.campus UBCV en_US
dc.description.scholarlevel Graduate en

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