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There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the trickiness of hyperparameter tuning.
An Open Source AutoML Benchmark
Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, and Joaquin Vanschoren. 2019 · 1907
Earlier work this paper cites.
Neural Network Ensembles
Lars Kai Hansen and Peter Salamon. 1990 · 1990
Earlier work this paper cites.
Stacked generalization
David H. Wolpert. 1992 · 1992
Earlier work this paper cites.
Bagging predictors
Leo Breiman. 1996 · 1996
Earlier work this paper cites.
Experiments with a New Boosting Algorithm. In Proceedings of the Thirteenth International Conference on International Conference on Machine Learning (Bari, Italy) (ICML’96) . Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 148–156
Yoav Freund and Robert E. Schapire. 1996 · 1996
Earlier work this paper cites.
The Lack of A Priori Distinctions Between Learning Algorithms
D. H. Wolpert. 1996 · 1996
Earlier work this paper cites.
SLURM: Simple Linux Utility for Resource Management
Andy B. Yoo, Morris A. Jette, and Mark Grondona. 2003 · 2003
Earlier work this paper cites.
Ensemble selection from libraries of models. In Machine Learning, Proceedings of the Twenty-first International Conference(ICML) (ACM International Conference Proceeding Series, Vol. 69) , Carla E. Brodley (Ed.). ACM
Rich Caruana, Alexandru Niculescu-Mizil, Geoff Crew, and Alex Ksikes. 2004 · 2004
Earlier work this paper cites.
Big data: The next frontier for innovation, competition and productivity
James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. 2011 · 2011
Earlier work this paper cites.
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 · 2011
Cited alongside, same era.
Ensemble Classifiers and Their Applications: A Review
Akhlaqur Rahman and Sumaira Tasnim. 2014 · 2014
Cited alongside, same era.
Efficient and robust automated machine learning. In Advances in Neural Information Processing Systems . 2962–2970
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015 · 2015
Cited alongside, same era.
Non-stochastic Best Arm Identification and Hyperparameter Optimization. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS) , Vol. 51. 240–248
Kevin G. Jamieson and Ameet Talwalkar. 2016 · 2016
Cited alongside, same era.
The age of analytics: competing in a data-driven world
Autostacker: a compositional evolutionary learning system. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) . ACM, 402–409
Boyuan Chen, Harvey Wu, Warren Mo, Ishanu Chattopadhyay, and Hod Lipson. 2018 · 2018
Later among the works it cites.
Iddo Drori, Yamuna Krishnamurthy, Raoni Lourenço, Rémi Rampin, Kyunghyun Cho, Cláudio T. Silva, and Juliana Freire. 2019 · 2019
Later among the works it cites.
Benchmark and Survey of Automated Machine Learning Frameworks
Marc-André Zöller and Marco F Huber. 2019 · 2019
Later among the works it cites.
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, and Alexander J. Smola. 2020 · 2020
Closest in time.
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McKinsey Analytics. 2016 · 2016
Cited alongside, same era.
TPOT: A tree-based pipeline optimization tool for automating machine learning. In Workshop on automatic machine learning . 66–74
Randal S Olson and Jason H Moore. 2016 · 2016
Cited alongside, same era.
Singularity: A Container System for HPC Applications
Dennis Gannon and Vanessa Sochat. 2017 · 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 · 2017
Cited alongside, same era.
Data science and analytics skills shortage: equipping the APEC workforce with the competencies demanded by employers
Claudia Pompa and Travis Burke. 2017 · 2017
Cited alongside, same era.
Super Learner
Mark J. van der Laan, Eric C. Polley, and Alan E. Hubbard. [n.d.]
Cited in the paper.
Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter. 2020 · 2020
Closest in time.
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering. In Conference on Knowledge Discovery and Data Mining . ACM, 2103–2113
Yuval Heffetz, Roman Vainshtein, Gilad Katz, and Lior Rokach. 2020 · 2020
Closest in time.
H2O AutoML: Scalable Automatic Machine Learning
Erin LeDell and Sebastien Poirier. 2020 · 2020
Closest in time.
Neural Ensemble Search for Performant and Calibrated Predictions
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter, and Yee Whye Teh. 2020 · 2020
Closest in time.