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Ensembling has a long history in statistical data analysis, with many impactful applications.
Classification and Regression Trees
Leo Breiman, Jerome Friedman, Charles J. Stone, and R.A. Olshen · 1984
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Inductive knowledge acquisition: A case study
J. R. Quinlan, P. J. Compton, K. A. Horn, and L. Lazarus · 1987
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Random forests
Leo Breiman · 2001
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Statistical mechanics of learning
A. Engel and C. P. L. Van den Broeck · 2001
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts · 2011
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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
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The mnist database of handwritten digit images for machine learning research
Li Deng · 2012
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Risk bounds for the majority vote: From a PAC-Bayesian analysis to a learning algorithm
Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario March, and Jean-Francis Roy · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Risk upper bounds for general ensemble methods with an application to multiclass classification
François Laviolette, Emilie Morvant, Liva Ralaivola, and Jean-Francis Roy · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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C. H. Martin and M. W. Mahoney · 2017
Cited alongside, same era.
Integrated qsar models to predict acute oral systemic toxicity
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
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Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, and Dmitry Vetrov · 2020
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Scaling description of generalization with number of parameters in deep learning
Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stéphane d’Ascoli, Giulio Biroli, Clément Hongler, and Matthieu Wyart · 2020
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Second order PAC-Bayesian bounds for the weighted majority vote
Andres Masegosa, Stephan Lorenzen, Christian Igel, and Yevgeny Seldin · 2020
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Dangers of Bayesian model averaging under covariate shift
Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, and Andrew G Wilson · 2021
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Davide Ballabio, Francesca Grisoni, Viviana Consonni, and Roberto Todeschini · 2019
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
M. Belkin, D. Hsu, S. Ma, and S. Mandal · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Cited alongside, same era.
Do ImageNet classifiers generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Cited alongside, same era.
What are Bayesian neural network posteriors really like?
Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, and Andrew Gordon Gordon Wilson · 2021
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Good classifiers are abundant in the interpolating regime
Ryan Theisen, Jason Klusowski, and Michael Mahoney · 2021
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Taxonomizing local versus global structure in neural network loss landscapes
Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E Gonzalez, Kannan Ramchandran, and Michael W Mahoney · 2021
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Deep ensembles work, but are they necessary?
Taiga Abe, E. Kelly Buchanan, Geoff Pleiss, Richard Zemel, and John Patrick Cunningham · 2022
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The best deep ensembles sacrifice predictive diversity
Taiga Abe, E Kelly Buchanan, Geoff Pleiss, and John Patrick Cunningham · 2022
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Diversity and generalization in neural network ensembles
Luis A. Ortega, Rafael Cabañas, and Andres Masegosa · 2022
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The multiBERTs: BERT reproductions for robustness analysis
Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra, Alexander D’Amour, Tal Linzen, Jasmijn Bastings, Iulia Raluca Turc, Jacob Eisenstein, Dipanjan Das, and Ellie Pavlick · 2022
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