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The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making.
The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, and Ryan J. Tibshirani · 1903
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Probability forecasting, 1986
A. P. Dawid · 1986
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Statistical calibration: a review
Christine Osborne · 1991
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Asymptotic calibration
Dean P Foster and Rakesh V Vohra · 1998
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Algorithmic learning in a random world
Vladimir Vovk, Alexander Gammerman, and Glenn Shafer · 2005
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E. Raftery · 2007
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Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery · 2007
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Reliability, sufficiency, and the decomposition of proper scores
Jochen Bröcker · 2009
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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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Estimating reliability and resolution of probability forecasts through decomposition of the empirical score
Jochen Bröcker · 2012
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Applied predictive modeling
Max Kuhn and Kjell Johnson · 2013
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Bayesian model selection based on proper scoring rules
A. Philip Dawid and Monica Musio · 2014
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Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration
Meelis Kull and Peter Flach · 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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Classifier Calibration
Peter A. Flach · 2016
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Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
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Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
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Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, and Thomas Schön · 2019
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Is distribution-free inference possible for binary regression?
Rina Foygel Barber · 2020
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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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Ursula Hébert-Johnson, Michael P Kim, Omer Reingold, and Guy N Rothblum · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
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Meelis Kull, Miquel Perello Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, and Peter Flach · 2019
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Calibration of Neural Networks using Splines
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Credit scoring using neural networks and SURE posterior probability calibration
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer · 2021
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Diagnostic uncertainty calibration: Towards reliable machine predictions in medical domain
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Revisiting the calibration of modern neural networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic · 2021
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Mitigating bias in calibration error estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, and Michael C Mozer · 2022
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