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A key challenge for deploying deep neural networks (DNNs) in safety critical settings is the need to provide rigorous ways to quantify their uncertainty.
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Interval estimation for a binomial proportion
Lawrence D Brown, T Tony Cai, and Anirban DasGupta · 2001
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Activity forecasting
Kris M Kitani, Brian D Ziebart, James Andrew Bagnell, and Martial Hebert · 2012
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Reachability-based safe learning with gaussian processes
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Distilling the knowledge in a neural network
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, et al · 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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Linear model predictive safety certification for learning-based control
Kim Wabersich and Melanie Zeilinger · 2018
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Skipnet: Learning dynamic routing in convolutional networks
Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E Gonzalez · 2018
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Safe planning via model predictive shielding, 2019
Osbert Bastani · 2019
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Robust guarantees for perception-based control, 2019
Sarah Dean, Nikolai Matni, Benjamin Recht, and Vickie Ye · 2019
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A general safety framework for learning-based control in uncertain robotic systems
J. F. Fisac, A. K. Akametalu, M. N. Zeilinger, S. Kaynama, J. Gillula, and C. J. Tomlin · 2019
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Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
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Conformal prediction under covariate shift
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Robust validation: Confident predictions even when distributions shift, 2020
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Robust model predictive shielding for safe reinforcement learning with stochastic dynamics
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