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Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI.
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An optimal reject rule for binary classifiers
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The Elements of Statistical Learning
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2001
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Pruning and dynamic scheduling of cost-sensitive ensembles
Wei Fan, Fang Chu, Haixun Wang, and Philip S. Yu · 2002
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Support vector machines with embedded reject option
Giorgio Fumera and Fabio Roli · 2002
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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On optimal reject rules and ROC curves
Carla M Santos-Pereira and Ana M Pires · 2005
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Classification with reject option
Radu Herbei and Marten H Wegkamp · 2006
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The interaction between classification and reject performance for distance-based reject-option classifiers
Thomas CW Landgrebe, David MJ Tax, Pavel Paclík, and Robert PW Duin · 2006
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Classification with a reject option using a hinge loss
Peter L Bartlett and Marten H Wegkamp · 2008
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Finding the homology of submanifolds with high confidence from random samples
Partha Niyogi, Stephen Smale, and Shmuel Weinberger · 2008
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Growing a multi-class classifier with a reject option
David MJ Tax and Robert PW Duin · 2008
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Support vector machines with a reject option
Yves Grandvalet, Alain Rakotomamonjy, Joseph Keshet, and Stéphane Canu · 2009
Classifying with confidence from incomplete information
Nathan Parrish, Hyrum S. Anderson, Maya R. Gupta, and Dun Yu Hsaio · 2013
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Optimal rates for k-NN density and mode estimation
Sanjoy Dasgupta and Samory Kpotufe · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Calibrated structured prediction
Volodymyr Kuleshov and Percy S Liang · 2015
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Optimal rates for plug-in estimators of density level sets
Philippe Rigollet, Régis Vert, et al · 2009
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Adaptive Hausdorff estimation of density level sets
Aarti Singh, Clayton Scott, Robert Nowak, et al · 2009
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Rates of convergence for the cluster tree
Kamalika Chaudhuri and Sanjoy Dasgupta · 2010
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On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
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Generalized density clustering
Alessandro Rinaldo and Larry Wasserman · 2010
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Shuying Liu and Weihong Deng · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2015
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Efficient learning by directed acyclic graph for resource constrained prediction
Joseph Wang, Kirill Trapeznikov, and Venkatesh Saligrama · 2015
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Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F Christiano, John Schulman, and Dan Mané · 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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Online learning with abstention
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, and Scott Yang · 2017
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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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What uncertainties do we need in Bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 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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On the safety of machine learning: Cyber-physical systems, decision sciences, and data products
Kush R Varshney and Homa Alemzadeh · 2017
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Nicolas Papernot and Patrick McDaniel · 2018
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