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Uncertainty quantification is a key component of machine learning models targeted at safety-critical systems such as in healthcare or autonomous vehicles.
The use of confidence or fiducial limits illustrated in the case of the binomial
Charles J Clopper and Egon S Pearson · 1934
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Determination of sample sizes for setting tolerance limits
Samuel S Wilks · 1941
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Statistical Tolerance Regions: Classical and Bayesian
I. Guttman · 1970
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Behavioral Risk Factor Surveillance System
Centers for Disease Control and Prevention (CDC) · 1984
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Evolutionary Principles in Self-Referential Learning. On Learning now to Learn: The Meta-Meta-Meta…-Hook
Jurgen Schmidhuber · 1987
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Learning by transduction
A Gammerman, V Vovk, and V Vapnik · 1998
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Inductive confidence machines for regression
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk, and Alex Gammerman · 2002
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Algorithmic learning in a random world
Vladimir Vovk, Alex Gammerman, and Glenn Shafer · 2005
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Conditional validity of inductive conformal predictors
Vladimir Vovk · 2013
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun · 2018
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Conformal prediction under covariate shift
Ryan J Tibshirani, Rina Foygel Barber, Emmanuel Candes, and Aaditya Ramdas · 2019
Pac confidence sets for deep neural networks via calibrated prediction
Sangdon Park, Osbert Bastani, Nikolai Matni, and Insup Lee · 2020
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Distribution-free, risk-controlling prediction sets
Stephen Bates, Anastasios Angelopoulos, Lihua Lei, Jitendra Malik, and Michael I Jordan · 2021
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Adaptive conformal inference under distribution shift, 2021
Isaac Gibbs and Emmanuel Candès · 2021
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Few-shot conformal prediction with auxiliary tasks, 2021
Adam Fisch, Tal Schuster, Tommi Jaakkola, and Regina Barzilay · 2021
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PAC prediction sets under covariate shift
Sangdon Park, Edgar Dobriban, Insup Lee, and Osbert Bastani · 2022
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Distribution-free prediction sets adaptive to unknown covariate shift
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Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Hongxiang Qiu, Edgar Dobriban, and Eric Tchetgen Tchetgen · 2022
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Distribution-free prediction sets for two-layer hierarchical models
Robin Dunn, Larry Wasserman, and Aaditya Ramdas · 2022
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