@inproceedings{guo_calibration_2017,
	title        = {On Calibration of Modern Neural Networks},
	author       = {Chuan Guo and Geoff Pleiss and Yu Sun and Kilian Q. Weinberger},
	year         = 2017,
	month        = {06--11 Aug},
	booktitle    = {Proceedings of the 34th International Conference on Machine Learning},
	publisher    = {PMLR},
	series       = {Proceedings of Machine Learning Research},
	volume       = 70,
	pages        = {1321--1330},
	editor       = {Precup, Doina and Teh, Yee Whye},
	pdf          = {http://proceedings.mlr.press/v70/guo17a/guo17a.pdf}
}
@article{krzakala2012probabilistic,
	title        = {Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices},
	author       = {Krzakala, Florent and M{\'e}zard, Marc and Sausset, Francois and Sun, Yifan and Zdeborov{\'a}, Lenka},
	year         = 2012,
	journal      = {Journal of Statistical Mechanics: Theory and Experiment},
	publisher    = {IOP Publishing},
	volume       = 2012,
	number       = {08},
	pages        = {P08009}
}
@article{decelle2011asymptotic,
	title        = {Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications},
	author       = {Decelle, Aurelien and Krzakala, Florent and Moore, Cristopher and Zdeborov{\'a}, Lenka},
	year         = 2011,
	journal      = {Physical Review E},
	publisher    = {APS},
	volume       = 84,
	number       = 6,
	pages        = {066106}
}
@article{dempster1977maximum,
	title        = {Maximum likelihood from incomplete data via the EM algorithm},
	author       = {Dempster, Arthur P and Laird, Nan M and Rubin, Donald B},
	year         = 1977,
	journal      = {Journal of the royal statistical society: series B (methodological)},
	publisher    = {Wiley Online Library},
	volume       = 39,
	number       = 1,
	pages        = {1--22}
}
@inproceedings{papadopoulos2002inductive,
	title        = {Inductive confidence machines for regression},
	author       = {Papadopoulos, Harris and Proedrou, Kostas and Vovk, Volodya and Gammerman, Alex},
	year         = 2002,
	booktitle    = {Machine Learning: ECML 2002: 13th European Conference on Machine Learning Helsinki, Finland, August 19--23, 2002 Proceedings 13},
	pages        = {345--356},
	organization = {Springer}
}
@article{measson2009generalized,
	title        = {The generalized area theorem and some of its consequences},
	author       = {M{\'e}asson, Cyril and Montanari, Andrea and Richardson, Thomas J and Urbanke, R{\"u}diger},
	year         = 2009,
	journal      = {IEEE Transactions on Information Theory},
	publisher    = {IEEE},
	volume       = 55,
	number       = 11,
	pages        = {4793--4821}
}
@inproceedings{frenkel_network_2021,
	title        = {Network Calibration by Class-based Temperature Scaling},
	author       = {Frenkel, Lior and Goldberger, Jacob},
	year         = 2021,
	booktitle    = {2021 29th European Signal Processing Conference (EUSIPCO)},
	volume       = {},
	number       = {},
	pages        = {1486--1490}
}
@article{zdeborova2016statistical,
	title        = {Statistical physics of inference: Thresholds and algorithms},
	author       = {Zdeborov{\'a}, Lenka and Krzakala, Florent},
	year         = 2016,
	journal      = {Advances in Physics},
	publisher    = {Taylor \& Francis},
	volume       = 65,
	number       = 5,
	pages        = {453--552}
}
@inproceedings{kull_beyond_2019,
	title        = {Beyond Temperature Scaling: Obtaining Well-Calibrated Multiclass Probabilities with Dirichlet Calibration},
	author       = {Kull, Meelis and Perello-Nieto, Miquel and K\"{a}ngsepp, Markus and Filho, Telmo Silva and Song, Hao and Flach, Peter},
	year         = 2019,
	publisher    = {Curran Associates Inc.},
        booktitle = {Proceedings of the 33rd International Conference on Neural Information Processing Systems},
	address      = {Red Hook, NY, USA},
	articleno    = 1103,
	numpages     = 11
}
@article{Sur2019,
	title        = {A modern maximum-likelihood theory for high-dimensional logistic regression},
	author       = {Pragya Sur and Emmanuel J. Cand{\`e}s},
	year         = 2019,
	journal      = {Proceedings of the National Academy of Sciences},
	volume       = 116,
	number       = 29,
	pages        = {14516--14525},
	eprint       = {https://www.pnas.org/doi/pdf/10.1073/pnas.1810420116}
}
@article{iba1999nishimori,
	title        = {The Nishimori line and Bayesian statistics},
	author       = {Iba, Yukito},
	year         = 1999,
	journal      = {Journal of Physics A: Mathematical and General},
	publisher    = {IOP Publishing},
	volume       = 32,
	number       = 21,
	pages        = 3875
}
@inproceedings{wang_rethinking_2021,
	title        = {Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence},
	author       = {Deng-Bao Wang and Lei Feng and Min-Ling Zhang},
	year         = 2021,
	booktitle    = {Neural Information Processing Systems}
}
@inproceedings{minderer_revisiting_2021,
	title        = {Revisiting the {Calibration} of {Modern} {Neural} {Networks}},
	author       = {Minderer, Matthias and Djolonga, Josip and Romijnders, Rob and Hubis, Frances and Zhai, Xiaohua and Houlsby, Neil and Tran, Dustin and Lucic, Mario},
	year         = 2021,
	booktitle    = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher    = {Curran Associates, Inc.},
	volume       = 34,
	pages        = {15682--15694},
	editor       = {Ranzato, M. and Beygelzimer, A. and Dauphin, Y. and Liang, P. S. and Vaughan, J. Wortman}
}
%% 

% for CIFAR10/100
@inproceedings{krizhevsky_learning_2009,
	title        = {Learning Multiple Layers of Features from Tiny Images},
	author       = {Alex Krizhevsky},
        booktitle    = {Technical report, University of Toronto},
	year         = 2009
}
% for SVHN
@inproceedings{netzer_reading_2011,
	title        = {Reading Digits in Natural Images with Unsupervised Feature Learning},
	author       = {Yuval Netzer and Tao Wang and Adam Coates and Alessandro Bissacco and Bo Wu and Andrew Y. Ng},
	year         = 2011,
	booktitle    = {NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011}
}
%%
@inproceedings{he_resnet_2016,
	title        = {Deep Residual Learning for Image Recognition},
	author       = {K. He and X. Zhang and S. Ren and J. Sun},
	year         = 2016,
	month        = {jun},
	booktitle    = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	publisher    = {IEEE Computer Society},
	address      = {Los Alamitos, CA, USA},
	volume       = {},
	pages        = {770--778},
	keywords     = {training;degradation;complexity theory;image recognition;neural networks;visualization;image segmentation}
}
@inproceedings{huang_densely_2017,
	title        = {Densely Connected Convolutional Networks},
	author       = {G. Huang and Z. Liu and L. Van Der Maaten and K. Q. Weinberger},
	year         = 2017,
	month        = {jul},
	booktitle    = {2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	publisher    = {IEEE Computer Society},
	address      = {Los Alamitos, CA, USA},
	volume       = {},
	pages        = {2261--2269},
	keywords     = {training;convolution;network architecture;convolutional codes;neural networks;road transportation}
}
@article{simonyan_very_2014,
	title        = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
	author       = {Karen Simonyan and Andrew Zisserman},
	year         = 2014,
	journal      = {CoRR},
	volume       = {abs/1409.1556}
}
@inproceedings{ding_repvgg_2021,
	title        = {Repvgg: Making vgg-style convnets great again},
	author       = {Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian},
	year         = 2021,
	booktitle    = {Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
	pages        = {13733--13742}
}
%% High dimensional logistic regression
@inproceedings{bai_dont_2021,
	title        = {Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification},
	author       = {Bai, Yu and Mei, Song and Wang, Huan and Xiong, Caiming},
	year         = 2021,
	month        = {18--24 Jul},
	booktitle    = {Proceedings of the 38th International Conference on Machine Learning},
	publisher    = {PMLR},
	series       = {Proceedings of Machine Learning Research},
	volume       = 139,
	pages        = {566--576},
	editor       = {Meila, Marina and Zhang, Tong},
	pdf          = {http://proceedings.mlr.press/v139/bai21c/bai21c.pdf}
}
@misc{clarte_theoretical_2022,
	title        = {Theoretical characterization of uncertainty in high-dimensional linear classification},
	author       = {Clarté, Lucas and Loureiro, Bruno and Krzakala, Florent and Zdeborová, Lenka},
	year         = 2022,
	publisher    = {arXiv:2202.03295},
	copyright    = {arXiv.org perpetual, non-exclusive license},
	keywords     = {Machine Learning (cs.LG), Disordered Systems and Neural Networks (cond-mat.dis-nn), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Physical sciences, FOS: Physical sciences}
}
@misc{clarte_study_2022,
	title        = {A study of uncertainty quantification in overparametrized high-dimensional models},
	author       = {Clarté, Lucas and Loureiro, Bruno and Krzakala, Florent and Zdeborová, Lenka},
	year         = 2022,
	publisher    = {arXiv:2210.12760},
	copyright    = {arXiv.org perpetual, non-exclusive license},
	keywords     = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}
}
@misc{Hodgkinson2022,
	title        = {Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes},
	author       = {Hodgkinson, Liam and van der Heide, Chris and Roosta, Fred and Mahoney, Michael W.},
	year         = 2022,
	publisher    = {arXiv:2210.07612},
	copyright    = {arXiv.org perpetual, non-exclusive license},
	keywords     = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}
}
@inproceedings{kristiadi_being_2020,
	title        = {Being Bayesian, Even Just a Bit, Fixes Overconfidence in {R}e{LU} Networks},
	author       = {Kristiadi, Agustinus and Hein, Matthias and Hennig, Philipp},
	year         = 2020,
	month        = {13--18 Jul},
	booktitle    = {Proceedings of the 37th International Conference on Machine Learning},
	publisher    = {PMLR},
	series       = {Proceedings of Machine Learning Research},
	volume       = 119,
	pages        = {5436--5446},
	editor       = {III, Hal Daumé and Singh, Aarti}
}
@misc{Mattei2019,
	title        = {A Parsimonious Tour of Bayesian Model Uncertainty},
	author       = {Mattei, Pierre-Alexandre},
	year         = 2019,
	publisher    = {arXiv:1902.05539},
	copyright    = {arXiv.org perpetual, non-exclusive license},
	keywords     = {Methodology (stat.ME), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.6, 62-01}
}
@inproceedings{graves_practical_2011,
	title        = {Practical Variational Inference for Neural Networks},
	author       = {Graves, Alex},
	year         = 2011,
	booktitle    = {Advances in Neural Information Processing Systems},
	publisher    = {Curran Associates, Inc.},
	volume       = 24,
	pages        = {},
	editor       = {J. Shawe-Taylor and R. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger}
}
@inproceedings{gal_dropout_2016,
	title        = {Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning},
	author       = {Gal, Yarin and Ghahramani, Zoubin},
	year         = 2016,
	booktitle    = {Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48},
	date         = {2016-10-04}
}
@inproceedings{laksh_simple_2017,
	title        = {Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles},
	author       = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
	year         = 2017,
	booktitle    = {Advances in Neural Information Processing Systems},
	publisher    = {Curran Associates, Inc.},
	volume       = 30,
	pages        = {},
	editor       = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett}
}
@inproceedings{maddox_simple_2019,
	title        = {A Simple Baseline for Bayesian Uncertainty in Deep Learning},
	author       = {Wesley J. Maddox and T. Garipov and Pavel Izmailov and Dmitry P. Vetrov and Andrew Gordon Wilson},
	year         = 2019,
	booktitle    = {NeurIPS}
}
@inproceedings{Lakshminarayanan_simple_2017,
	title        = {Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles},
	author       = {Lakshminarayanan, Balaji and Pritzel, Alexander and Blundell, Charles},
	year         = 2017,
	booktitle    = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
	location     = {Long Beach, California, USA},
	publisher    = {Curran Associates Inc.},
	address      = {Red Hook, NY, USA},
	series       = {NIPS'17},
	pages        = {6405–6416},
	numpages     = 12
}
% Calibration methods
@article{platt_probabilistic_2000,
	title        = {Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods},
	author       = {Platt, John},
	year         = 2000,
	month        = {06},
	journal      = {Adv. Large Margin Classif.},
	volume       = 10,
	pages        = {}
}
% for histogram bining
@article{zadrozny_obtaining_2001,
	title        = {Obtaining Calibrated Probability Estimates from Decision Trees and Naive Bayesian Classifiers},
	author       = {Zadrozny, Bianca and Elkan, Charles},
	year         = 2001,
	month        = {05},
	journal      = {ICML},
	volume       = 1,
	pages        = {}
}
% for bayesian histogram binning
@inproceedings{naeini_obtaining_2015,
	title        = {Obtaining Well Calibrated Probabilities Using Bayesian Binning},
	author       = {Naeini, Mahdi Pakdaman and Cooper, Gregory F. and Hauskrecht, Milos},
	year         = 2015,
	booktitle    = {Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},
	location     = {Austin, Texas},
	publisher    = {AAAI Press},
	series       = {AAAI'15},
	pages        = {2901–2907},
	numpages     = 7
}
% for isotonic regression
@inproceedings{zadrozny_transforming_2022,
	title        = {Transforming Classifier Scores into Accurate Multiclass Probability Estimates},
	author       = {Zadrozny, Bianca and Elkan, Charles},
	year         = 2002,
	location     = {Edmonton, Alberta, Canada},
	publisher    = {Association for Computing Machinery},
	address      = {New York, NY, USA},
	series       = {KDD '02},
	pages        = {694–699},
	numpages     = 6
}
%% Reviews
@article{abdar_review_2021,
	title        = {A review of uncertainty quantification in deep learning: Techniques, applications and challenges},
	author       = {Moloud Abdar and Farhad Pourpanah and Sadiq Hussain and Dana Rezazadegan and Li Liu and Mohammad Ghavamzadeh and Paul Fieguth and Xiaochun Cao and Abbas Khosravi and U. Rajendra Acharya and Vladimir Makarenkov and Saeid Nahavandi},
	year         = 2021,
	journal      = {Information Fusion},
	volume       = 76,
	pages        = {243--297},
	keywords     = {Artificial intelligence, Uncertainty quantification, Deep learning, Machine learning, Bayesian statistics, Ensemble learning}
}
@misc{Gawlikowski2021,
	title        = {A Survey of Uncertainty in Deep Neural Networks},
	author       = {Gawlikowski, Jakob and Tassi, Cedrique Rovile Njieutcheu and Ali, Mohsin and Lee, Jongseok and Humt, Matthias and Feng, Jianxiang and Kruspe, Anna and Triebel, Rudolph and Jung, Peter and Roscher, Ribana and Shahzad, Muhammad and Yang, Wen and Bamler, Richard and Zhu, Xiao Xiang},
	year         = 2021,
	publisher    = {arXiv:2107.03342},
	copyright    = {Creative Commons Attribution 4.0 International},
	keywords     = {Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences}
}
%% 

% impossibility results for asymptotic calibration, conformal prediction
@inproceedings{gupta_distribution_2020,
	title        = {Distribution-Free Binary Classification: Prediction Sets, Confidence Intervals and Calibration},
	author       = {Gupta, Chirag and Podkopaev, Aleksandr and Ramdas, Aaditya},
	year         = 2020,
	booktitle    = {Proceedings of the 34th International Conference on Neural Information Processing Systems},
	location     = {Vancouver, BC, Canada},
	publisher    = {Curran Associates Inc.},
	address      = {Red Hook, NY, USA},
	series       = {NIPS'20},
	articleno    = 313,
	numpages     = 13
}
@book{vovk_algorithmic_2005,
	title        = {Algorithmic Learning in a Random World},
	author       = {Vovk, Vladimir and Gammerman, Alex and Shafer, Glenn},
	year         = 2005,
	publisher    = {Springer-Verlag},
	address      = {Berlin, Heidelberg}
}


@inproceedings{loureiro_learning_2021,
	title        = {Learning curves of generic features maps for realistic datasets with a teacher-student model},
	author       = {Loureiro, Bruno and Gerbelot, Cedric and Cui, Hugo and Goldt, Sebastian and Krzakala, Florent and Mezard, Marc and Zdeborov\'{a}, Lenka},
	year         = 2021,
	booktitle    = {Advances in Neural Information Processing Systems},
	publisher    = {Curran Associates, Inc.},
	volume       = 34,
	pages        = {18137--18151},
	editor       = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan}
}


@article{hendrycks_benchmarking_2019,
  author       = {Dan Hendrycks and
                  Thomas G. Dietterich},
  title        = {Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  journal      = {ICLR},
  year         = {2019},
}
