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Ensembles are a straightforward, remarkably effective method for improving the accuracy,calibration, and robustness of models on classification tasks; yet, the reasons that underlie their success remain an active area of research.
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L.K. Hansen and P. Salamon · 1990
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Stuart Geman, Elie Bienenstock, and René Doursat · 1992
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Peter Hall · 1992
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Bradley Efron and Robert J Tibshirani · 1994
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Leo Breiman · 1996
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Tom Heskes · 1998
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Thomas G. Dietterich · 2000
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Pedro Domingos · 2000
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Jakob Vogdrup Hansen and Tom Heskes · 2000
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Joint and separate convexity of the Bregman distance
Heinz H. Bauschke and Jonathan M. Borwein · 2001
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Gareth M James · 2003
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Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, and Joydeep Ghosh · 2005
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Thomas G. Dietterich · 2005
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Prediction, learning, and games
Nicolo Cesa-Bianchi and Gábor Lugosi · 2006
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Primal-dual subgradient methods for convex problems
Yurii E. Nesterov · 2009
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A Generalized Bias-Variance Decomposition for Bregman Divergences
David Pfau · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Generalized ambiguity decompositions for classification with applications in active learning and unsupervised ensemble pruning
Zhengshen Jiang, Hongzhi Liu, Bin Fu, and Zhonghai Wu · 2017
Cited alongside, same era.
Generalized negative correlation learning for deep ensembling
Sebastian Buschjäger, Lukas Pfahler, and Katharina Morik · 2020
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Double trouble in double descent : Bias and variance(s) in the lazy regime
Stéphane d’Ascoli, Maria Refinetti, Giulio Biroli, and Florent Krzakala · 2020
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Evaluating scalable bayesian deep learning methods for robust computer vision
Fredrik K Gustafsson, Martin Danelljan, and Thomas B Schön · 2020
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On power laws in deep ensembles
Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, and Dmitry Vetrov · 2020
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Learning under model misspecification: Applications to variational and ensemble methods
Andres Masegosa · 2020
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
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A bias-variance decomposition for Bayesian deep learning
James A. Brofos and Rui Shu · 2019
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
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A modern take on the bias-variance tradeoff in neural networks
Brady Neal, Sarthak Mittal, Aristide Baratin, Vinayak Tantia, Matthew Scicluna, Simon Lacoste-Julien, and Ioannis Mitliagkas · 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, D. Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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To ensemble or not ensemble: When does end-to-end training fail?
Andrew Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan Iliescu, Mikel Lujan, and Gavin Brown · 2020
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Hyperparameter ensembles for robustness and uncertainty quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
Andrew Gordon Wilson and Pavel Izmailov · 2020
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Rethinking bias-variance trade-off for generalization of neural networks
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, and Yi Ma · 2020
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Deep ensembles from a Bayesian perspective
Lara Hoffmann and Clemens Elster · 2021
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Unifying mirror descent and dual averaging
Anatoli Juditsky, Joon Kwon, and Éric Moulines · 2021
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On the reversed bias-variance tradeoff in deep ensembles
Seijin Kobayashi, Johannes von Oswald, and Benjamin Grewe · 2021
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Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, and Dustin Tran · 2021
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Diversity and generalization in neural network ensembles
Luis A Ortega, Rafael Cabañas, and Andrés R Masegosa · 2021
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Scaling vision with sparse mixture of experts
Carlos Riquelme Ruiz, Joan Puigcerver, Basil Mustafa, Maxim Neumann, Rodolphe Jenatton, André Susano Pinto, Daniel Keysers, and Neil Houlsby · 2021
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Neural ensemble search for performant and calibrated predictions
Sheheryar Zaidi, Arber Zela, Thomas Elsken, Chris Holmes, Frank Hutter, and Yee Whye Teh · 2021
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