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We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters.
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Michael˜A Osborne, Roman Garnett and Stephen˜J Roberts · 2009
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“Sparse Spectrum Gaussian Process Regression”
Miguel L“’azaro-Gredilla, Joaquin Qui“˜nonero Candela, Carl˜Edward Rasmussen and An“’bal˜R. Figueiras-Vidal · 2010
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James Bergstra, R“’emi Bardenet, Yoshua Bengio and Bal“’azs K“’egl · 2011
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Frank Hutter, Holger˜H. Hoos and Kevin Leyton-Brown · 2011
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James Bergstra, Daniel Yamins and David Cox · 2013
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“Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms”
Chris Thornton, Frank Hutter, Holger˜H. Hoos and Kevin Leyton-Brown · 2013
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Robert˜B Gramacy, Matt Taddy and Stefan˜M Wild · 2013
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James Hensman, Nicolo Fusi and Neil˜D Lawrence · 2013
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“Agentswitch: Towards smart energy tariff selection”
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Max Welling and Yee˜W Teh · 2011
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“Scikit-learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“Practical Bayesian Optimization of Machine Learning Algorithms”
Jasper Snoek, Hugo Larochelle and Ryan˜Prescott Adams · 2012
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“Julia: A Fast Dynamic Language for Technical Computing”
J. Bezanson, S. Karpinski, V.˜B. Shah and A. Edelman · 2012
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SD Ramchurn et al · 2013
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“An Efficient Approach for Assessing Hyperparameter Importance”
Frank Hutter, Holger Hoos and Kevin Leyton-Brown · 2014
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“Hilbert Space Methods for Reduced-Rank Gaussian Process Regression”, 2014
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