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Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem.
Algorithms for hyper-parameter optimization
J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 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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A few useful things to know about machine learning
P. M. Domingos · 2012
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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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Automated machine learning on big data using stochastic algorithm tuning
T. Nickson, M. A. Osborne, S. Reece, and S. J. Roberts · 2014
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Freeze-thaw bayesian optimization
K. Swersky, J. Snoek, and R. P. Adams · 2014
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Net2net: Accelerating learning via knowledge transfer
T. Chen, I. Goodfellow, and J. Shlens · 2015
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Efficient and robust automated machine learning
M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter · 2015
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Deep feature synthesis: Towards automating data science endeavors
J. M. Kanter and K. Veeramachaneni · 2015
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Non-stochastic best arm identification and hyperparameter optimization
K. Jamieson and A. Talwalkar · 2016
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Explorekit: Automatic feature generation and selection
G. Katz, E. C. R. Shin, and D. Song · 2016
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Cognito: Automated feature engineering for supervised learning
U. Khurana, D. Turaga, H. Samulowitz, and S. Parthasrathy · 2016
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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 · 2016
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Hyperband: Bandit-based configuration evaluation for hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2016
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Evaluation of a tree-based pipeline optimization tool for automating data science
R. S. Olson, N. Bartley, R. J. Urbanowicz, and J. H. Moore · 2016
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Genetic programming for feature construction and selection in classification on high-dimensional data
B. Tran, B. Xue, and M. Zhang · 2016
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
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Efficient neural architecture search via parameters sharing
H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean · 2018
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Darwinml: A graph-based evolutionary algorithm for automated machine learning
F. Qi, Z. Xia, G. Tang, H. Yang, Y. Song, G. Qian, X. An, C. Lin, and G. Shi · 2018
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Taking human out of learning applications: A survey on automated machine learning
Y. Quanming, W. Mengshuo, J. E. Hugo, G. Isabelle, H. Yi-Qi, L. Yu-Feng, T. Wei-Wei, Y. Qiang, and Y. Yang · 2018
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2018
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ProxylessNAS: Direct neural architecture search on target task and hardware
H. Cai, L. Zhu, and S. Han · 2019
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Autolearn: Automated feature generation and selection
A. Kaul, S. Maheshwary, and V. Pudi · 2017
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Autostacker: A compositional evolutionary learning system
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Bohb: Robust and efficient hyperparameter optimization at scale
S. Falkner, A. Klein, and F. Hutter · 2018
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Auto-keras: Efficient neural architecture search with network morphism, 2018
H. Jin, Q. Song, and X. Hu · 2018
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Neural architecture search with bayesian optimisation and optimal transport
K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. P. Xing · 2018
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Feature engineering for predictive modeling using reinforcement learning
U. Khurana, H. Samulowitz, and D. Turaga · 2018
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Neural architecture optimization
R. Luo, F. Tian, T. Qin, E. Chen, and T.-Y. Liu · 2018
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Neural architecture search: A survey
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Automating predictive modeling process using reinforcement learning
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DARTS: Differentiable architecture search
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Adanet: A scalable and flexible framework for automatically learning ensembles, 2019
C. Weill, J. Gonzalvo, V. Kuznetsov, S. Yang, S. Yak, H. Mazzawi, E. Hotaj, G. Jerfel, V. Macko, B. Adlam, M. Mohri, and C. Cortes · 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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Autocross: Automatic feature crossing for tabular data in real-world applications
L. Yuanfei, W. Mengshuo, Z. Hao, Y. Quanming, T. WeiWei, C. Yuqiang, Y. Qiang, and D. Wenyuan · 2019
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