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Research progress in AutoML has lead to state of the art solutions that can cope quite wellwith supervised learning task, e.g., classification with AutoSklearn.
Openml: Networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, and Luis Torgo · 1931
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Beyond incremental processing: Tracking concept drift
Jeffrey C Schlimmer and Richard H Granger · 1986
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Model selection and akaike’s information criterion (aic): The general theory and its analytical extensions
Hamparsum Bozdogan · 1987
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Lifelong robot learning
Sebastian Thrun and Tom M Mitchell · 1995
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The parallel transfer of task knowledge using dynamic learning rates based on a measure of relatedness
Daniel L Silver and Robert E Mercer · 1996
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Learning in the presence of concept drift and hidden contexts
Gerhard Widmer and Miroslav Kubat · 1996
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Gradient-based optimization of hyperparameters
Yoshua Bengio · 2000
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Mining time-changing data streams
Geoff Hulten, Laurie Spencer, and Pedro Domingos · 2001
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Selective functional transfer: Inductive bias from related tasks
Daniel Silver and Robert Mercer · 2001
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A streaming ensemble algorithm (sea) for large-scale classification
W. Nick Street and YongSeog Kim · 2001
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The task rehearsal method of life-long learning: Overcoming impoverished data
Daniel L Silver and Robert E Mercer · 2002
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Learning with drift detection
João Gama, Pedro Medas, Gladys Castillo, and Pedro Rodrigues · 2004
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Early drift detection method
Manuel Baena-García, José del Campo-Ávila, Raúl Fidalgo, Albert Bifet, Ricard Gavaldà, and Rafael Morales-Bueno · 2006
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Learning from time-changing data with adaptive windowing
Albert Bifet and Ricard Gavalda · 2007
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Particle swarm model selection
Hugo Jair Escalante, Manuel Montes, and Luis Enrique Sucar · 2009
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Automated configuration of algorithms for solving hard computational problems
Frank Hutter · 2009
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Leveraging bagging for evolving data streams
Albert Bifet, Geoff Holmes, and Bernhard Pfahringer · 2010
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Change with delayed labeling: When is it detectable?
Indre Žliobaite · 2010
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Algorithms for hyper-parameter optimization
James S Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl · 2011
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2011
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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, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay · 2011
Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
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Design of the 2015 chalearn automl challenge
Isabelle Guyon, Kristin Bennett, Gavin C. Cawley, Hugo Jair Escalante, Sergio Escalera, Tin Kam Ho, Núria Macià, Bisakha Ray, Mehreen Saeed, Alexander R. Statnikov, and Evelyne Viegas · 2015
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Intelligent adaptive ensembles for data stream mining: a high return on investment approach
M Kehinde Olorunnimbe, Herna L Viktor, and Eric Paquet · 2015
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Having a blast: Meta-learning and heterogeneous ensembles for data streams
Jan N van Rijn, Geoffrey Holmes, Bernhard Pfahringer, and Joaquin Vanschoren · 2015
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Designing neural network architectures using reinforcement learning
Bowen Baker, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar · 2016
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Cited alongside, same era.
Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Lifelong machine learning systems: Beyond learning algorithms
Daniel L. Silver and Qiang Yang amd Lianghao Li · 2013
Cited alongside, same era.
Auto-weka: Combined selection and hyperparameter optimization of classification algorithms
Chris Thornton, Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2013
Cited alongside, same era.
A survey on concept drift adaptation
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia · 2014
Cited alongside, same era.
Metastream: A meta-learning based method for periodic algorithm selection in time-changing data
André Luis Debiaso Rossi, André Carlos Ponce de Leon Ferreira, Carlos Soares, Bruno Feres De Souza, et al · 2014
Cited alongside, same era.
Freeze-thaw bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan Prescott Adams · 2014
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Learning cumulatively to become more knowledgeable
Geli Fei, Shuai Wang, and Bing Liu · 2016
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A brief review of the chalearn automl challenge: Any-time any-dataset learning without human intervention
Isabelle Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, Núria Macià, Bisakha Ray, Lukasz Romaszko, Michèle Sebag, Alexander R. Statnikov, Sébastien Treguer, and Evelyne Viegas · 2016
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Fast hoeffding drift detection method for evolving data streams
Ali Pesaranghader and Herna L. Viktor · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Ali Pesaranghader, Herna Viktor, and Eric Paquet · 2017
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, and Alex Kurakin · 2017
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Lifelong learning for sentiment classification
Zhiyuan Chen, Nianzu Ma, and Bing Liu · 2018
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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, Michele Sebag, Alexander Statnikov, Wei-Wei Tu, and Evelyne Viegas · 2018
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Instance spaces for machine learning classification
Mario A Muñoz, Laura Villanova, Davaatseren Baatar, and Kate Smith-Miles · 2018
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