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Selective classification allows models to abstain from making predictions (e.g., say "I don't know") when in doubt in order to obtain better effective accuracy.
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
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To reject or not to reject: That is the question - an answer in case of neural classifiers
Claudio De Stefano, Carlo Sansone, and Mario Vento · 2000
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Estimating the support of a high-dimensional distribution
Bernhard Schölkopf, John C. Platt, John C. Shawe-Taylor, Alex J. Smola, and Robert C. Williamson · 2001
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Bianca Zadrozny and Charles Elkan · 2001
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Vladimir Vovk, Ilia Nouretdinov, and Alexander Gammerman · 2003
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Input-dependent estimation of generalization error under covariate shift
Masashi Sugiyama and Klaus-Robert Müller · 2005
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Algorithmic Learning in a Random World
Vladimir Vovk, Alex Gammerman, and Glenn Shafer · 2005
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Classification with reject option
Radu Herbei and Marten H. Wegkamp · 2006
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Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E. Raftery · 2007
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Stochastic Orders
M. Shaked and J.G. Shanthikumar · 2007
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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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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Dataset Shift in Machine Learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence · 2009
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On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
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Reduced lung-cancer mortality with low-dose computed tomographic screening
Denise R Aberle, Amanda M Adams, Christine D Berg, William C Black, Jonathan D Clapp, Richard M Fagerstrom, Ilana F Gareen, Constantine Gatsonis, Pamela M Marcus, JoRean D Sicks, National Lung Screening Trial Research Team, and Reginald F. Munden · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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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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Calibrating predictive model estimates to support personalized medicine
Xiaoqian Jiang, Melanie Osl, Jihoon Kim, and Lucila Ohno-Machado · 2012
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
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Computed-aided diagnosis (cad) in the detection of breast cancer
C. Dromain, B. Boyer, R. Ferré, S. Canale, S. Delaloge, and C. Balleyguier · 2013
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Venn-abers predictors
Vladimir Vovk and Ivan Petej · 2014
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Robust learning under uncertain test distributions: Relating covariate shift to model misspecification
Junfeng Wen, Chun-Nam Yu, and Russell Greiner · 2014
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Fast two-sample testing with analytic representations of probability measures
Kacper P Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, and Arthur Gretton · 2015
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Calibrated structured prediction
Volodymyr Kuleshov and Percy Liang · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory F. Cooper, and Milos Hauskrecht · 2015
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Doubly robust covariate shift correction
Sashank Reddi, Barnabas Poczos, and Alex Smola · 2015
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From data to optimal decision making: A data-driven, probabilistic machine learning approach to decision support for patients with sepsis
Randaugment: Practical automated data augmentation with a reduced search space
Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, and Quoc Le · 2020
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Distribution-free binary classification: prediction sets, confidence intervals and calibration
Chirag Gupta, Aleksandr Podkopaev, and Aaditya Ramdas · 2020
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AugMix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Selective question answering under domain shift
Amita Kamath, Robin Jia, and Percy Liang · 2020
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Ordered sgd: A new stochastic optimization framework for empirical risk minimization
Kenji Kawaguchi and Haihao Lu · 2020
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Large-scale methods for distributionally robust optimization
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Athanasios Tsoukalas, Timothy Albertson, and Ilias Tagkopoulos · 2015
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Concrete problems in ai safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 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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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 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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Current applications and future impact of machine learning in radiology
Garry Choy, Omid Khalilzadeh, Mark Michalski, Synho Do, Anthony E. Samir, Oleg S. Pianykh, J. Raymond Geis, Pari V. Pandharipande, James A. Brink, and Keith J. Dreyer · 2018
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Daniel Levy, Yair Carmon, John C Duchi, and Aaron Sidford · 2020
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Consistent estimators for learning to defer to an expert
Hussein Mozannar and David Sontag · 2020
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Calibrating deep neural networks using focal loss
Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, and Puneet Dokania · 2020
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A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2020
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Distributionally robust neural networks
Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang · 2020
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Uncertainty estimation using a single deep deterministic neural network
Joost van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Individual calibration with randomized forecasting
Shengjia Zhao, Tengyu Ma, and Stefano Ermon · 2020
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Classification with rejection based on cost-sensitive classification
Nontawat Charoenphakdee, Zhenghang Cui, Yivan Zhang, and Masashi Sugiyama · 2021
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Selective classification can magnify disparities across groups
Erik Jones, Shiori Sagawa, Pang Wei Koh, Ananya Kumar, and Percy Liang · 2021
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Soft calibration objectives for neural networks
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael Curtis Mozer, and Rebecca Roelofs · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
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Just train twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
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Revisiting the calibration of modern neural networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Ann Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic · 2021
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Selective regression under fairness criteria
Abhin Shah, Yuheng Bu, Joshua Ka-Wing Lee, Subhro Das, Rameswar Panda, Prasanna Sattigeri, and Gregory W Wornell · 2021
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On calibration and out-of-domain generalization
Yoav Wald, Amir Feder, Daniel Greenfeld, and Uri Shalit · 2021
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Conformal risk control
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