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We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions.
Constant depth circuits, fourier transform, and learnability
Nathan Linial, Yishay Mansour, and Noam Nisan · 1993
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Learning Boolean Functions via the Fourier Transform
Yishay Mansour · 1994
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Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
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Gradient-based optimization of hyperparameters
Yoshua Bengio · 2000
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Noise-tolerant learning, the parity problem, and the statistical query model
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Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information
E. J. Candes, J. Romberg, and T. Tao · 2006
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Compressed sensing
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Compressive sensing and structured random matrices
Holger Rauhut · 2010
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Algorithms for hyper-parameter optimization
James S. Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Learning sparse boolean polynomials
Sahand Negahban and Devavrat Shah · 2012
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Learning fourier sparse set functions
Peter Stobbe and Andreas Krause · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George E. Dahl, and Geoffrey E. Hinton · 2013
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Multi-task bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams · 2013
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Bayesian optimization in high dimensions via random embeddings
Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, and Nando de Freitas · 2013
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An Improved Estimate in the Restricted Isometry Problem
Jean Bourgain · 2014
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Bayesian optimization with inequality constraints
Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, and John P. Cunningham · 2014
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Scalable gradient-based tuning of continuous regularization hyperparameters
Jelena Luketina, Mathias Berglund, Klaus Greff, and Tapani Raiko · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan P. Adams · 2015
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Jie Fu, Hongyin Luo, Jiashi Feng, Kian Hsiang Low, and Tat-Seng Chua · 2016
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, and Kilian Q. Weinberger · 2016
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The restricted isometry property of subsampled fourier matrices
Ishay Haviv and Oded Regev · 2016
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Analysis of Boolean Functions
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Jasper Snoek, Kevin Swersky, Richard S. Zemel, and Ryan P. Adams · 2014
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The list-decoding size of fourier-sparse boolean functions
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Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
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The news on auto-tuning
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The parallel knowledge gradient method for batch bayesian optimization
Jian Wu and Peter I. Frazier · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Efficient hyperparameter optimization for deep learning algorithms using deterministic rbf surrogates
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