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Hyperparameter optimization aims to find the optimal hyperparameter configuration of a machine learning model, which provides the best performance on a validation dataset.
A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
H. J. Kushner · 1964
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The application of Bayesian methods for seeking the extremum
J. Moćkus, V. Tiesis, and A. Źilinskas · 1978
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Comparison of three methods for selecting values of input variables in the analysis of output from a computer code
M. D. McKay, R. J. Beckman, and W. J. Conover · 1979
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Evolutionary Principles in Self-Referential Learning
J. Schmidhuber · 1987
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Signature verification using a “Siamese” time delay neural network
J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah · 1994
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Machine learning, neural and statistical classification
D. Michie, D. J. Spiegelhalter, and C. C. Taylor · 1994
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Efficient global optimization of expensive black-box functions
D. R. Jones, M. Schonlau, and W. J. Welch · 1998
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Learning to Learn
S. Thrun and L. Pratt · 1998
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Meta-learning by landmarking various learning algorithms
B. Pfahringer, H. Bensusan, and C. Giraud-Carrier · 2000
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Caltech-256 Object Category Dataset
G. Griffin, A. Holub, and P. Perona · 2007
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Multi-task Gaussian process prediction
E. V. Bonilla, K. M. A. Chai, and C. K. I. Williams · 2008
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E. Brochu, V. M. Cora, and N. de Freitas · 2010
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Gaussian process optimization in the bandit setting: No regret and experimental design
N. Srinivas, A. Krause, S. Kakade, and M. Seeger · 2010
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Algorithms for hyper-parameter optimization
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 2011
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Extracting speaker-specific information with a regularized Siamese deep network
K. Chen and A. Salman · 2011
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results, 2012
M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
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Learning hyperparameter optimization initializations
M. Wistuba, N. Schilling, and L. Schmidt-Thieme · 2015
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Towards a neural statistician
H. Edwards and A. Storkey · 2016
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AutoML Challenge: AutoML framework using random space partitioning optimizer
J. Kim, J. Jeong, and S. Choi · 2016
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Siamese recurrent architectures for learning sentence simiarity
J. Mueller and A. Thyagarajan · 2016
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Warm starting Bayesian optimization
M. Poloczek, J. Wang, and P. I. Frazier · 2016
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Bayesian optimization with robust Bayesian neural networks
J. T. Springenberg, A. Klein, S. Falkner, and F. Hutter · 2016
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Entropy search for information-efficient global optimization
P. Hennig and C. J. Schuler · 2012
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Practical Bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
Cited alongside, same era.
Collaborative hyperparameter tuning
R. Bardenet, M. Brendel, B. Kégl, and M. Sebag · 2013
Cited alongside, same era.
Multi-task Bayesian optimization
K. Swersky, J. Snoek, and R. P. Adams · 2013
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Efficient transfer learning method for automatic hyperparameter tuning
D. Yogatama and G. Mann · 2014
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Initializing Bayesian hyperparameter optimization via meta-learning
M. Feurer, J. T. Springerberg, and F. Hutter · 2015
Cited alongside, same era.
GPyOpt: A Bayesian optimization framework in Python, 2016
The GPyOpt authors · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, and D. Wierstra · 2016
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Learning to learn without gradient descent by gradient descent
Y. Chen, M. W. Hoffman, S. G. Colmenarejo, M. Denil, T. P. Lillicrap, M. Botvinick, and N. de Freitas · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly
Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata · 2017
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