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Survey on automated machine learning
Marc-André Zöller and Marco F. Huber · 1910
Earlier work this paper cites.
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
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Earlier work this paper cites.
Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
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Science and data science
David M Blei and Padhraic Smyth · 2017
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50 years of data science
David Donoho · 2017
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Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Taking human out of learning applications: A survey on automated machine learning
Quanming Yao, Mengshuo Wang, Hugo Jair Escalante, Isabelle Guyon, Yi-Qi Hu, Yu-Feng Li, Wei-Wei Tu, Qiang Yang, and Yang Yu · 2018
Cited alongside, same era.
Automatic machine learning by pipeline synthesis using model-based reinforcement learning and a grammar
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni de Paula Lourenco, Kyunghyun Cho, Claudio Silva, and Juliana Freire · 2019
Cited alongside, same era.
Automl: A survey of the state-of-the-art
Xin He, Kaiyong Zhao, and Xiaowen Chu · 2019
Closest in time.
Fast task-aware architecture inference
Efi Kokiopoulou, Anja Hauth, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, and Jesse Berent · 2019
Closest in time.
TPOT: A tree-based pipeline optimization tool for automating machine learning
Randal S Olson and Jason H Moore · 2019
Closest in time.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Closest in time.
OBOE: Collaborative filtering for AutoML model selection
Chengrun Yang, Yuji Akimoto, Dae Won Kim, and Madeleine Udell · 2019
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GitHub repo for AutoML using metadata language embeddings: data, models, and code
Iddo Drori, Lu Liu, Yi Nian, Sharath Koorathota, Jie Li, Antonio Khalil Moretti, Juliana Freire, and Madeleine Udell · 2019
Cited alongside, same era.
An open source AutoML benchmark
P. Gijsbers, E. LeDell, S. Poirier, J. Thomas, B. Bischl, and J. Vanschoren · 2019
Cited alongside, same era.