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In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
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A tutorial on thompson sampling
Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen · 1935
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Verification of forecasts expressed in terms of probability
Glenn W Brier et al · 1950
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani · 1994
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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The elements of statistical learning: data mining, inference, and prediction , volume 2
Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep exploration via bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Ensemble sampling
Xiuyuan Lu and Benjamin Van Roy · 2017
Cited alongside, same era.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Vikranth Dwaracherla and Benjamin Van Roy · 2020
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Hypermodels for exploration
Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Ian Osband, Zheng Wen, and Benjamin Van Roy · 2020
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Bayesian deep ensembles via the neural tangent kernel
Bobby He, Balaji Lakshminarayanan, and Yee Whye Teh · 2020
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Why are bootstrapped deep ensembles not better?
Jeremy Nixon, Balaji Lakshminarayanan, and Dustin Tran · 2020
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Evaluating probabilistic inference in deep learning: Beyond marginal predictions
Xiuyuan Lu, Ian Osband, Benjamin Van Roy, and Zheng Wen · 2021
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Randomized prior functions for deep reinforcement learning
Ian Osband, John Aslanides, and Albin Cassirer · 2018
Cited alongside, same era.
Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan · 2019
Cited alongside, same era.
Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
Cited alongside, same era.
The neural testbed: Evaluating predictive distributions, 2022a
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O’Donoghue, and Benjamin Van Roy
Cited in the paper.
Evaluating high-order predictive distributions in deep learning, 2022b
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, and Benjamin Van Roy
Cited in the paper.
Ian Osband, Zheng Wen, Mohammad Asghari, Morteza Ibrahimi, Xiyuan Lu, and Benjamin Van Roy · 2021
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Beyond marginal uncertainty: How accurately can bayesian regression models estimate posterior predictive correlations?
Chaoqi Wang, Shengyang Sun, and Roger Grosse · 2021
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From predictions to decisions: The importance of joint predictive distributions
Zheng Wen, Ian Osband, Chao Qin, Xiuyuan Lu, Morteza Ibrahimi, Vikranth Dwaracherla, Mohammad Asghari, and Benjamin Van Roy · 2021
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An analysis of ensemble sampling
Chao Qin, Zheng Wen, Xiuyuan Lu, and Benjamin Van Roy · 2022
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