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In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning.
On Bayesian methods for seeking the extremum
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Gaussian processes for global optimization
Osborne, Michael A, Garnett, Roman, and Roberts, Stephen J · 2009
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Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, Niranjan, Krause, Andreas, Kakade, Sham M, and Seeger, Matthias · 2009
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Collaborative expert portfolio management
Stern, D H, Samulowitz, H, Herbrich, R, Graepel, T, and others · 2010
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Sequential model-based optimization for general algorithm configuration
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Snoek, Jasper, Larochelle, Hugo, and Adams, Ryan P · 2012
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Feurer, Matthias, Klein, Aaron, Eggensperger, Katharina, Springenberg, Jost, Blum, Manuel, and Hutter, Frank · 2015
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Hyperparameter optimization with factorized multilayer perceptrons
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Wistuba, Martin, Schilling, Nicolas, and Schmidt-Thieme, Lars · 2015
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A brief review of the chalearn automl challenge: Any-time any-dataset learning without human intervention
Guyon, Isabelle, Chaabane, Imad, Escalante, Hugo Jair, Escalera, Sergio, Jajetic, Damir, Lloyd, James Robert, Macià, Núria, Ray, Bisakha, Romaszko, Lukasz, Sebag, Michèle, Statnikov, Alexander, Treguer, Sébastien, and Viegas, Evelyne · 2016
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Efficient hyperparameter optimization and infinitely many armed bandits
Li, Lisha, Jamieson, Kevin, DeSalvo, Giulia, Rostamizadeh, Afshin, and Talwalkar, Ameet
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Shahriari, Bobak, Swersky, Kevin, Wang, Ziyu, Adams, Ryan P, and de Freitas, Nando · 2016
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