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Overconfidence and underconfidence in machine learning classifiers is measured by calibration: the degree to which the probabilities predicted for each class match the accuracy of the classifier on that prediction.
Analyzing the role of model uncertainty for electronic health records
Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, and Andrew M. Dai · 1906
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Verification of Forecasts Expressed In Terms of Probability
Glenn W. Brier · 1950
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Verification of probabilistic predictions: A brief review
Allan H Murphy and Edward S Epstein · 1967
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Goodness of fit tests for the multiple logistic regression model
David W Hosmer and Stanley Lemesbow · 1980
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The well-calibrated bayesian
A Philip Dawid · 1982
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The comparison and evaluation of forecasters
Morris H DeGroot and Stephen E Fienberg · 1983
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
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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
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Cited alongside, same era.
Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery · 2007
Cited alongside, same era.
Calibrating predictive model estimates to support personalized medicine
Xiaoqian Jiang, Melanie Osl, Jihoon Kim, and Lucila Ohno-Machado · 2011
Cited alongside, same era.
Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
Cited alongside, same era.
Posterior calibration and exploratory analysis for natural language processing models
Khanh Nguyen and Brendan O’Connor · 2015
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2017
Later among the works it cites.
Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas G Dietterich · 2018
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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
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Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
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Athanasios Tsoukalas, Timothy Albertson, and Ilias Tagkopoulos · 2015
Cited alongside, same era.
Assessing calibration of prognostic risk scores
Cynthia S Crowson, Elizabeth J Atkinson, and Terry M Therneau · 2016
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert Kleinberg, Sendhil Mullainathan, and Jon Kleinberg · 2018
Later among the works it cites.
Learning for single-shot confidence calibration in deep neural networks through stochastic inferences
Seonguk Seo, Paul Hongsuck Seo, and Bohyung Han · 2019
Closest in time.
On mixup training: Improved calibration and predictive uncertainty for deep neural networks
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, and Sarah Michalak · 2019
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Evaluating model calibration in classification
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, and Thomas B Schön · 2019
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