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Ensembles are widely used in machine learning and, usually, provide state-of-the-art performance in many prediction tasks.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Neural network ensembles
Lars Kai Hansen and Peter Salomon · 1990
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Neural networks and the bias/variance dilemma
Stuart Geman, Elie Bienenstock, and René Doursat · 1992
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Neural network ensembles, cross validation and active learning
Anders Krogh and Jesper Vedelsby · 1994
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Bagging predictors
Leo Breiman · 1996
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Experiments with a new boosting algorithm
Yoav Freund and Robert E. Schapire · 1996
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Some PAC-Bayesian theorems
David McAllester · 1998
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Ensemble learning via negative correlation
Yong Liu and Xin Yao · 1999
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Diversity versus quality in classification ensembles based on feature selection
Padraig Cunningham and John Carney · 2000
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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Methods for designing multiple classifier systems
Fabio Roli, Giorgio Giacinto, and Gianni Vernazza · 2001
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PAC-Bayes & margins
John Langford and John Shawe-Taylor · 2002
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PAC-Bayesian generalization error bounds for Gaussian process classification
Matthias Seeger · 2002
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Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
Ludmila I Kuncheva and Christopher J Whitaker · 2003
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Divace: Diverse and accurate ensemble learning algorithm
Arjun Chandra and Xin Yao · 2004
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Managing diversity in regression ensembles
Gavin Brown, Jeremy L Wyatt, and Peter Tiňo · 2005
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An analysis of diversity measures
E Ke Tang, Ponnuthurai N Suganthan, and Xin Yao · 2006
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An information theoretic perspective on multiple classifier systems
Gavin Brown · 2009
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Modeling wine preferences by data mining from physicochemical properties., 2009
Paulo Cortez, Antonio Cerdeira, Fernando Almeida, Telmo Matos, and Jose Reis · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Cited alongside, same era.
Ensemble pruning via individual contribution ordering
Zhenyu Lu, Xindong Wu, Xingquan Zhu, and Josh Bongard · 2010
Cited alongside, same era.
Multi-information ensemble diversity
Zhi-Hua Zhou and Nan Li · 2010
Cited alongside, same era.
From PAC-Bayes bounds to quadratic programs for majority votes
François Laviolette, Mario Marchand, and Jean-Francis Roy · 2011
Cited alongside, same era.
Diversity regularized machine
Yang Yu, Yu-Feng Li, and Zhi-Hua Zhou · 2011
Cited alongside, same era.
Stochastic multiple choice learning for training diverse deep ensembles
Stefan Lee, Senthil Purushwalkam, Michael Cogswell, Viresh Ranjan, David J Crandall, and Dhruv Batra · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 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
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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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An efficient ensemble pruning approach based on simple coalitional games
Hadjer Ykhlef and Djamel Bouchaffra · 2017
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The variance drain and Jensen’s inequality
Robert A Becker · 2012
Cited alongside, same era.
Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
Cited alongside, same era.
Mining actionlet ensemble for action recognition with depth cameras
Jiang Wang, Zicheng Liu, Ying Wu, and Junsong Yuan · 2012
Cited alongside, same era.
Ensemble methods: foundations and algorithms
Zhi-Hua Zhou · 2012
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Cited alongside, same era.
Kaggle lshtc4 winning solution
Antti Puurula, Jesse Read, and Albert Bifet · 2014
Cited alongside, same era.
Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
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Sharpening Jensen’s inequality
JG Liao and Arthur Berg · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
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Can you trust your model’s uncertainty? Evaluating predictive uncertainty under dataset shift
Jasper Snoek, Yaniv Ovadia, Emily Fertig, Balaji Lakshminarayanan, Sebastian Nowozin, D Sculley, Joshua Dillon, Jie Ren, and Zachary Nado · 2019
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Batchensemble: an alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2019
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Cyclical stochastic gradient mcmc for bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 2019
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Generalized negative correlation learning for deep ensembling
Sebastian Buschjäger, Lukas Pfahler, and Katharina Morik · 2020
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Maximizing overall diversity for improved uncertainty estimates in deep ensembles
Siddhartha Jain, Ge Liu, Jonas Mueller, and David Gifford · 2020
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Learning under model misspecification: Applications to variational and ensemble methods
Andrés R Masegosa · 2020
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Second order PAC-Bayesian bounds for the weighted majority vote
Andrés R Masegosa, Stephan S Lorenzen, Christian Igel, and Yevgeny Seldin · 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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