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We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.
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OpenML: Networked science in machine learning
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Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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B. Shahriari, K. Swersky, Z. Wang, R. Adams, and N. de Freitas · 2016
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P. Chrabaszcz, I. Loshchilov, and F. Hutter · 2017
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Google vizier: A service for black-box optimization
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley · 2017
Deep Gaussian processes for multi-fidelity modeling
K. Cutajar, M. Pullin, A. Damianou, N. Lawrence, and J. Gonzáles · 2018
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BOHB: Robust and efficient hyperparameter optimization at scale
S. Falkner, A. Klein, and F. Hutter · 2018
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Parallelised Bayesian optimisation via Thompson sampling
K. Kandasamy, A. Krishnamurthy, J. Schneider, and B. Poczos · 2018
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Massively parallel hyperparameter tuning
L. Li, K. Jamieson, A. Rostamizadeh, K. Gonina, M. Hardt, B. Recht, and A. Talwalkar · 2018
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Tune: A research platform for distributed model selection and training
R. Liaw, E. Liang, R. Nishihara, P. Moritz, J. E. Gonzalez, and I. Stoica · 2018
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Fast Bayesian hyperparameter optimization on large datasets
A. Klein, S. Falkner, S. Bartels, P. Hennig, and F. Hutter · 2017
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Learning curve prediction with Bayesian neural networks
A. Klein, S. Falkner, J. T. Springenberg, and F. Hutter · 2017
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Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2017
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Regularizing and optimizing LSTM language models
S. Merity, N. S. Keskar, and R. Socher · 2017
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Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin · 2017
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Multi-information source optimization
M. Poloczek, J. Wang, and P. Frazier · 2018
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Asynchronous batch Bayesian optimisation with improved local penalisation
A. Alvin, B. Ru, J. P. Calliess, S. Roberts, and M. A. Osborne · 2019
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DARTS: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
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NAS-Bench-101: Towards reproducible neural architecture search
C. Ying, A. Klein, E. Real, E. Christiansen, K. Murphy, and F. Hutter · 2019
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Nas-bench-201: Extending the scope of reproducible neural architecture search
X. Dong and Y. Yang · 2020
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NAS valuation is frustratingly hard
A. Yang, P. M. Esperança, and F. M. Carlucci · 2020
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