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Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes with more evidence.
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, and Eytan Bakshy · 1910
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Truth and probability. reprinted in
FP Ramsey · 1926
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A basis for the selection of a response surface design
George EP Box and Norman R Draper · 1959
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A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise
H. J. Kushner · 1964
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The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
Harry L Morgan · 1965
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On bayesian methods for seeking the extremum
Jonas Močkus · 1975
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Science and statistics
George EP Box · 1976
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Information, prediction, and query by committee
Yoav Freund, H Sebastian Seung, Eli Shamir, and Naftali Tishby · 1992
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A practical bayesian framework for backpropagation networks
David JC MacKay · 1992
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Query by committee
H Sebastian Seung, Manfred Opper, and Haim Sompolinsky · 1992
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani · 1994
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Gaussian processes for regression
C. K. Williams and C. Rasmussen · 1995
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Bagging predictors
Leo Breiman · 1996
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Efficient global optimization of expensive black-box functions
Donald R Jones, Matthias Schonlau, and William J Welch · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cdna hybridizations
Mei-Ling Ting Lee, Frank C Kuo, GA Whitmore, and Jeffrey Sklar · 2000
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A bayesian a-optimal and model robust design criterion
Xiaojie Zhou, Lawrence Joseph, David B Wolfson, and Patrick Bélisle · 2003
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Uncertainty, entropy, variance and the effect of partial information
James V Zidek and Constance Van Eeden · 2003
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Query by committee made real
Ran Gilad-Bachrach, Amir Navot, and Naftali Tishby · 2005
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Algorithmic learning in a random world
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer · 2005
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Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
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A novel automated lazy learning qsar (all-qsar) approach: method development, applications, and virtual screening of chemical databases using validated all-qsar models
Shuxing Zhang, Alexander Golbraikh, Scott Oloff, Harold Kohn, and Alexander Tropsha · 2006
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Active learning for regression based on query by committee
Robert Burbidge, Jem J Rowland, and Ross D King · 2007
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Meet P. Vadera, Adam D. Cobb, Borhan Jalaeian, and Benjamin M Marlin · 2007
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Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2008
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
Pedro J Ballester and John BO Mitchell · 2010
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Treatment of tuberculosis: guidelines
World Health Organization and Stop TB Initiative · 2010
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Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M Kakade, and Matthias Seeger · 2010
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Handbook of markov chain monte carlo
Steve Brooks, Andrew Gelman, Galin Jones, and Xiao-Li Meng · 2011
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Convergence rates of efficient global optimization algorithms
Adam D Bull · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee Whye Teh · 2011
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A connectionist machine for genetic hillclimbing , volume 28
David Ackley · 2012
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The safe bayesian
Peter Grünwald · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
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The bernstein-von-mises theorem under misspecification
Bas JK Kleijn and Aad W van der Vaart · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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Bayesian inference with misspecified models
Stephen G Walker · 2013
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Active learning: A survey
Charu C Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and S Yu Philip · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration
Meelis Kull and Peter Flach · 2015
Single-model uncertainties for deep learning
Natasa Tagasovska and David Lopez-Paz · 2019
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Learning loss for active learning
Donggeun Yoo and I. Kweon · 2019
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Drugcomb: an integrative cancer drug combination data portal
Bulat Zagidullin, Jehad Aldahdooh, Shuyu Zheng, Wenyu Wang, Yinyin Wang, Joseph Saad, Alina Malyutina, Mohieddin Jafari, Ziaurrehman Tanoli, Alberto Pessia, et al · 2019
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Deep evidential regression
Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus · 2020
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Uncertainty sets for image classifiers using conformal prediction
Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan, and Jitendra Malik · 2020
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Depth uncertainty in neural networks
Javier Antoran, James Allingham, and José Miguel Hernández-Lobato · 2020
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Current status and prospects of hiv treatment
Tomas Cihlar and Marshall Fordyce · 2016
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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 residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 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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How many biological replicates are needed in an rna-seq experiment and which differential expression tool should you use?
Nicholas J Schurch, Pietá Schofield, Marek Gierliński, Christian Cole, Alexander Sherstnev, Vijender Singh, Nicola Wrobel, Karim Gharbi, Gordon G Simpson, Tom Owen-Hughes, et al · 2016
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Beyond pinball loss: Quantile methods for calibrated uncertainty quantification
Youngseog Chung, Willie Neiswanger, Ian Char, and Jeff Schneider · 2020
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Efficient and scalable bayesian neural nets with rank-1 factors
Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Y. Ma, Jasper Snoek, K. Heller, Balaji Lakshminarayanan, and Dustin Tran · 2020
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Model misspecification, bayesian versus credibility estimation, and gibbs posteriors
Liang Hong and Ryan Martin · 2020
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A new perspective on uncertainty quantification of deep ensembles
S. Hu, Nicola Pezzotti, and M. Welling · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Jeremiah Zhe Liu, Zian Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan · 2020
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Ensemble distribution distillation
Andrey Malinin, Bruno Mlodozeniec, and Mark Gales · 2020
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Learning under model misspecification: Applications to variational and ensemble methods
Andres Masegosa · 2020
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Behaviour suite for reinforcement learning
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, and Hado van Hasselt · 2020
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Real-time uncertainty decomposition for online learning control
Jonas Umlauft, Armin Lederer, T. Beckers, and S. Hirche · 2020
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Simple and scalable epistemic uncertainty estimation using a single deep deterministic neural network
Joost R. van Amersfoort, L. Smith, Y. Teh, and Yarin Gal · 2020
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Batchensemble: An alternative approach to efficient ensemble and lifelong learning
Yeming Wen, Dustin Tran, and Jimmy Ba · 2020
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Cyclical stochastic gradient mcmc for bayesian deep learning
Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, and Andrew Gordon Wilson · 2020
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates · 2021
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