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Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to predict the ground-truth probabilities, which is indeed calibrated.
Verification of forecasts expressed in terms of probability
Glenn W Brier et al · 1950
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Admissible probability measurement procedures
Emir H. Shuford, Arthur Albert, and H. Edward Massengill · 1966
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Elicitation of personal probabilities and expectations
Leonard J. Savage · 1971
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The well-calibrated bayesian
A Philip Dawid · 1982
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Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
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A general method for comparing probability assessors
Mark J. Schervish · 1989
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Probable networks and plausible predictions-a review of practical bayesian methods for supervised neural networks
David JC MacKay · 1995
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Asymptotic calibration
Dean P. Foster and Rakesh V. Vohra · 1998
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John C. Platt · 1999
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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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Loss functions for binary class probability estimation and classification: Structure and applications
Andreas Buja, Werner Stuetzle, and Yi Shen · 2005
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Predicting good probabilities with supervised learning
Alexandru Niculescu-Mizil and Rich Caruana · 2005
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Deterministic calibration and nash equilibrium
Sham Kakade and Dean Foster · 2008
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Composite binary losses
Mark D. Reid and Robert C. Williamson · 2010
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Practical variational inference for neural networks
Alex Graves · 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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In search of the real inductive bias: On the role of implicit regularization in deep learning
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2014
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Uncertainty in Deep Learning
Yarin Gal · 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.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2017
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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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Universality of deep convolutional neural networks
Ding-Xuan Zhou · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, et al · 2021
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Soft calibration objectives for neural networks
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C Mozer, and Becca Roelofs · 2021
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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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Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Meelis Kull, Telmo Silva Filho, and Peter Flach · 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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The expressive power of neural networks: A view from the width
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, and Liwei Wang · 2017
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Implicit regularization in deep learning
Behnam Neyshabur · 2017
Cited alongside, same era.
Multiclass classification, information, divergence and surrogate risk
John Duchi, Khashayar Khosravi, and Feng Ruan · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
Cited alongside, same era.
Trainable calibration measures for neural networks from kernel mean embeddings
Aviral Kumar, Sunita Sarawagi, and Ujjwal Jain · 2018
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Towards an empirical theory of deep learning
Preetum Nakkiran · 2021
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Why model calibration matters, and how to measure it, 2021
Lee Richardson and Taylor Pospisil · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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The calibration generalization gap
Annabelle Carrell, Neil Mallinar, James Lucas, and Preetum Nakkiran · 2022
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Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, and Udi Wieder · 2022
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Low-degree multicalibration
Parikshit Gopalan, Michael P. Kim, Mihir Singhal, and Shengjia Zhao · 2022
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Omnipredictors for constrained optimization
Lunjia Hu, Inbal Livni-Navon, Omer Reingold, and Chutong Yang · 2022
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Smooth ECE: Principled reliability diagrams via kernel smoothing
Jarosław Błasiok and Preetum Nakkiran · 2023
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Multicalibration as boosting for regression
Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, and Jessica Sorrell · 2023
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Loss Minimization Through the Lens Of Outcome Indistinguishability
Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, and Udi Wieder · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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