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Automatic machine learning is an important problem in the forefront of machine learning.
OBOE: Collaborative filtering for AutoML initialization
Chengrun Yang, Yuji Akimoto, Dae Won Kim, and Madeleine Udell · 1905
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Three models for the description of language
Noam Chomsky · 1956
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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OpenML: networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo · 2014
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Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg, Manuel Blum, and Frank Hutter · 2015
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TPOT: A tree-based pipeline optimization tool for automating machine learning
Randal Olson and Jason Moore · 2016
Cited alongside, same era.
Thinking fast and slow with deep learning and tree search
Thomas Anthony, Zheng Tian, and David Barber · 2017
Cited alongside, same era.
Recipe: a grammar-based framework for automatically evolving classification pipelines
Alex GC de Sá, Walter José GS Pinto, Luiz Otavio VB Oliveira, and Gisele L Pappa · 2017
Cited alongside, same era.
Layered TPOT: Speeding up tree-based pipeline optimization
Pieter Gijsbers, Joaquin Vanschoren, and Randal S Olson · 2017
Cited alongside, same era.
Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA
Lars Kotthoff, Chris Thornton, Holger H Hoos, Frank Hutter, and Kevin Leyton-Brown · 2017
Cited alongside, same era.
End-to-end training of differentiable pipelines across machine learning frameworks
AlphaD3M: Machine learning pipeline synthesis
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni de Paula Lourenco, Jorge Piazentin Ono, Kyunghyun Cho, Claudio Silva, and Juliana Freire · 2018
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Probabilistic matrix factorization for automated machine learning
Nicolo Fusi, Rishit Sheth, and Melih Elibol · 2018
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A general reinforcement learning algorithm that masters chess, Shogi, and Go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Code for automatic machine learning by pipeline synthesis using model-based reinforcement learning and a grammar, 2019
Iddo Drori, Yamuna Krishnamurthy, Remi Rampin, Raoni de Paula Lourenco, Kyunghyun Cho, Claudio Silva, and Juliana Freire · 2019
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Mitar Milutinovic, Atılım Günes Baydi, Robert Zinkov, William Harvey, Dawn Song, and Frank Wood · 2017
Cited alongside, same era.
ATM: A distributed, collaborative, scalable system for automated machine learning
Thomas Swearingen, Will Drevo, Bennett Cyphers, Alfredo Cuesta-Infante, Arun Ross, and Kalyan Veeramachaneni · 2017
Cited alongside, same era.
Autostacker: A compositional evolutionary learning system
Boyuan Chen, Harvey Wu, Warren Mo, Ishanu Chattopadhyay, and Hod Lipson · 2018
Cited alongside, same era.
Analysis of the automl challenge series 2015-2018
Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera, Zhengying Liu, Damir Jajetic, Bisakha Ray, Mehreen Saeed, Michéle Sebag, Alexander Statnikov, WeiWei Tu, and Evelyne Viegas · 2019
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Automated Machine Learning: Methods, Systems, Challenges
Frank Hutter, Lars Kotthoff, and Joaquin Vanschoren · 2019
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