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Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors.
Forecasting precipitation in percentages of probability
Cleve Hallenbeck · 1920
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On estimating regression
E. A. Nadaraya · 1964
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Smooth regression analysis
Geoffrey S. Watson · 1964
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Statistical Inference Under Order Restrictions: The Theory and Application of Isotonic Regression
R.E. Barlow · 1972
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Reliability of subjective probability forecasts of precipitation and temperature
Allan H Murphy and Robert L Winkler · 1977
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The well-calibrated bayesian
A Philip Dawid · 1982
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Plotting p against x
JB Copas · 1983
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The comparison and evaluation of forecasters
Morris H DeGroot and Stephen E Fienberg · 1983
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Histogram regression estimation using data-dependent partitions
Andrew Nobel · 1996
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Smoothing Methods in Statistics
J.S. Simonoff · 1996
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Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers
Bianca Zadrozny and Charles Elkan · 2001
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Transforming classifier scores into accurate multiclass probability estimates
Bianca Zadrozny and Charles Elkan · 2002
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Verifying probability of precipitation - an example from finland
Pertti Nurmi · 2003
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Probabilistic forecasts, calibration and sharpness
Tilmann Gneiting, Fadoua Balabdaoui, and Adrian E Raftery · 2007
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Some remarks on the reliability of categorical probability forecasts
Jochen Bröcker · 2008
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Deterministic calibration and nash equilibrium
Sham Kakade and Dean Foster · 2008
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Two extra components in the brier score decomposition
David B Stephenson, Caio AS Coelho, and Ian T Jolliffe · 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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Binary classifier calibration: Non-parametric approach
Mahdi Pakdaman Naeini, Gregory F Cooper, and Milos Hauskrecht · 2014
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Calibration of pre-trained transformers
Shrey Desai and Greg Durrett · 2020
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Reliability diagrams
Matthijs Hollemans · 2020
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Mark Tygert · 2020
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Calibration tests beyond classification
David Widmann, Fredrik Lindsten, and Dave Zachariah · 2020
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Stable reliability diagrams for probabilistic classifiers
Timo Dimitriadis, Tilmann Gneiting, and Alexander I Jordan · 2021
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Forecast hedging and calibration
Dean P Foster and Sergiu Hart · 2021
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Smooth calibration, leaky forecasts, finite recall, and nash dynamics
Dean P. Foster and Sergiu Hart · 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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Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
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Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Hao Song, Peter Flach, et al · 2019
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Verified uncertainty calibration
Ananya Kumar, Percy S Liang, and Tengyu Ma · 2019
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A comparison of flare forecasting methods. ii. benchmarks, metrics, and performance results for operational solar flare forecasting systems
KD Leka, Sung-Hong Park, Kanya Kusano, Jesse Andries, Graham Barnes, Suzy Bingham, D Shaun Bloomfield, Aoife E McCloskey, Veronique Delouille, David Falconer, et al · 2019
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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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Metrics of calibration for probabilistic predictions
Imanol Arrieta-Ibarra, Paman Gujral, Jonathan Tannen, Mark Tygert, and Cherie Xu · 2022
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Low-degree multicalibration
Parikshit Gopalan, Michael P. Kim, Mihir Singhal, and Shengjia Zhao · 2022
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T-cal: An optimal test for the calibration of predictive models
Donghwan Lee, Xinmeng Huang, Hamed Hassani, and Edgar Dobriban · 2022
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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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A unifying theory of distance from calibration
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, and Preetum Nakkiran · 2023
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When does optimizing a proper loss yield calibration?, 2023
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, and Preetum Nakkiran · 2023
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Evaluating probabilistic classifiers: The triptych
Timo Dimitriadis, Tilmann Gneiting, Alexander I Jordan, and Peter Vogel · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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