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While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, a recent trend in AutoML is to focus on neural architecture search.
Application of Bayesian approach to numerical methods of global and stochastic optimization
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Evolving neural networks through augmenting topologies
K. O. Stanley and R. Miikkulainen · 2002
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Ensemble selection from libraries of models
R. Caruana, A. Niculescu-Mizil, G. Crew, and A. Ksikes · 2004
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Getting the most out of ensemble selection
R. Caruana, A. Munson, and A. Niculescu-Mizil · 2006
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Extremely randomized trees
P. Geurts, D. Ernst, and L. Wehenkel · 2006
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Hydra: Automatically configuring algorithms for portfolio-based selection
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Hydra-MIP: Automated algorithm configuration and selection for mixed integer programming
L. Xu, F. Hutter, H. Hoos, and K. Leyton-Brown · 2011
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Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
C. Thornton, F. Hutter, H. Hoos, and K. Leyton-Brown · 2013
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Towards an empirical foundation for assessing Bayesian optimization of hyperparameters
K. Eggensperger, M. Feurer, F. Hutter, J. Bergstra, J. Snoek, H. Hoos, and K. Leyton-Brown · 2013
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Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn
B. Komer, J. Bergstra, and C. Eliasmith · 2014
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An efficient approach for assessing hyperparameter importance
F. Hutter, H. Hoos, and K. Leyton-Brown · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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OpenML: Networked science in machine learning
J. Vanschoren, J. van Rijn, B. Bischl, and L. Torgo · 2014
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Initializing Bayesian hyperparameter optimization via meta-learning
M. Feurer, T. Springenberg, and F. Hutter · 2015
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Design of the 2015 chalearn automl challenge
I. Guyon, K. Bennett, G. Cawley, H. J. Escalante, S. Escalera, Tin Kam Ho, N. Macià, B. Ray, M. Saeed, A. Statnikov, and E. Viegas · 2015
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Efficient and robust automated machine learning
M. Feurer, A. Klein, K. Eggensperger, J. T. Springenberg, M. Blum, and F. Hutter · 2015
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The reusable holdout: Preserving validity in adaptive data analysis
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth · 2015
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Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
R. Olson, N. Bartley, R. Urbanowicz, and J. Moore · 2016
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Towards automatically-tuned neural networks
H. Mendoza, A. Klein, M. Feurer, J. Springenberg, and F. Hutter · 2016
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ASlib: A benchmark library for algorithm selection
B. Bischl, P. Kerschke, L. Kotthoff, M. Lindauer, Y. Malitsky, A. Frechétte, H. Hoos, F. Hutter, K. Leyton-Brown, K. Tierney, and J. Vanschoren · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Non-stochastic best arm identification and hyperparameter optimization
K. Jamieson and A. Talwalkar · 2016
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation
I. Tsamardinos, E. Greasidou, and G. Borboudakis · 2018
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Automatic Machine Learning: Methods, Systems, Challenges
F. Hutter, L. Kotthoff, and J. Vanschoren, editors · 2019
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Auto-keras: An efficient neural architecture search system
H. Jin, Q. Song, and X.Hu · 2019
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Neural architecture search: A survey
T. Elsken, J. Metzen, and F. Hutter · 2019
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NAS-Bench-101: towards reproducible neural architecture search
C. Ying, A. Klein, E. Christiansen, E. Real, K. Murphy, and F. Hutter · 2019
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DARTS: differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
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Automatic differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Shake-shake regularization of 3-branch residual networks
X. Gastaldi · 2017
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Fast Bayesian optimization of machine learning hyperparameters on large datasets
A. Klein, S. Falkner, S. Bartels, P. Hennig, and F. Hutter · 2017
Cited alongside, same era.
Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
Cited alongside, same era.
LightGBM: A highly efficient gradient boosting decision tree
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Liu · 2017
Cited alongside, same era.
Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
A Zela, A. Klein, S. Falkner, and F. Hutter · 2018
Cited alongside, same era.
Hyperparameter importance for image classification by residual neural networks
A. Sharma, J. van Rijn, F. Hutter, and A. Müller · 2019
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BOAH: A tool suite for multi-fidelity bayesian optimization & analysis of hyperparameters
M. Lindauer, K. Eggensperger, M. Feurer, A. Biedenkapp, J. Marben, P. Müller, and F. Hutter · 2019
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Autonomio
M. Kotila · 2019
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Shakedrop regularization for deep residual learning
Y. Yamada, M. Iwamura, T. Akiba, and K. Kise · 2019
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An open source AutoML benchmark
P. Gijsbers, E. LeDell, J. Thomas, S. Poirier, B. Bischl, and J. Vanschoren · 2019
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Searching for a robust neural architecture in four gpu hours
X. Dong and Y. Yang · 2019
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OpenML-Python: an extensible Python API for OpenML
M. Feurer, J. N. van Rijn, A. Kadra, P. Gijsbers, N. Mallik, S. Ravi, A. Müller, J. Vanschoren, and F. Hutter · 2019
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NAS evaluation is frustratingly hard
A. Yang, P. Esperana, and F. Carlucci · 2020
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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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Understanding and robustifying differentiable architecture search
A. Zela, T. Elsken, T. Saikia, Y. Marrakchi, T. Brox, and F. Hutter · 2020
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Best practices for scientific research on neural architecture search
M. Lindauer and F. Hutter · 2020
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NAS-Bench-1Shot1: benchmarking and dissecting one-shot neural architecture search
A. Zela, J. Siems, and F. Hutter · 2020
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AutoGluon-Tabular: Robust and accurate automl for structured data
N. Erickson, J. Mueller, A. Shirkov, H. Zhang, P. Larroy, M. Li, and A. Smola · 2020
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Auto-sklearn 2.0: The next generation
M. Feurer, K. Eggensperger, S. Falkner, M. Lindauer, and F. Hutter · 2020
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