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Hyperparameter tuning is an active area of research in machine learning, where the aim is to identify the optimal hyperparameters that provide the best performance on the validation set.
Mathematical programs with optimization problems in the constraints
Jerome Bracken and James T McGill · 1973
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Jerome Sacks, William J Welch, Toby J Mitchell, and Henry P Wynn · 1989
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Efficient global optimization of expensive black-box functions
Donald R Jones, Matthias Schonlau, and William J Welch · 1998
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Gradient-based optimization of hyperparameters
Yoshua Bengio · 2000
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Foundations of bilevel programming
Stephan Dempe · 2002
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Springer New York, New York, NY, 2006
Penalty and Augmented Lagrangian Methods · 2006
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The theory of the market economy
Heinrich Von Stackelberg · 2007
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Bilevel optimization and machine learning
K. P. Bennett, G. Kunapuli, J. Hu, and J. Pang · 2008
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 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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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 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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Practical bilevel optimization: algorithms and applications
Jonathan F Bard · 2013
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
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A review on bilevel optimization: From classical to evolutionary approaches and applications
Ankur Sinha, Pekka Malo, and Kalyanmoy Deb · 2017
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimilano Pontil · 2018
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Stochastic hyperparameter optimization through hypernetworks
Jonathan Lorraine and David Duvenaud · 2018
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Bilevel optimization based on kriging approximations of lower level optimal value function
Ankur Sinha, Samish Bedi, and Kalyanmoy Deb · 2018
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Scalable bayesian optimization using deep neural networks
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams · 2015
Cited alongside, same era.
Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
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Matthew MacKay, Paul Vicol, Jon Lorraine, David Duvenaud, and Roger Grosse · 2019
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Penalty method for inversion-free deep bilevel optimization
Akshay Mehra and Jihun Hamm · 2019
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http://archive.ics.uci.edu/ml/datasets/communities+and+crime
Uci machine learning repository: Communities and crime data set · 2020
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Bilevel optimization based on iterative approximation of multiple mappings
Ankur Sinha, Zhichao Lu, Kalyanmoy Deb, and Pekka Malo · 2020
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