
@article{Bender2001,
title = {Adjusting for multiple testing—when and how?},
journal = {Journal of Clinical Epidemiology},
volume = {54},
number = {4},
pages = {343-349},
year = {2001},
issn = {0895-4356},
author = {Ralf Bender and Stefan Lange},
}

@article{Arrieta-Ibarra2022,
  author  = {Imanol Arrieta-Ibarra and Paman Gujral and Jonathan Tannen and Mark Tygert and Cherie Xu},
  title   = {Metrics of Calibration for Probabilistic Predictions},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {351},
  pages   = {1--54},
}

@inproceedings{Gupta2021,
title={Calibration of Neural Networks using Splines},
author={Kartik Gupta and Amir Rahimi and Thalaiyasingam Ajanthan and Thomas Mensink and Cristian Sminchisescu and Richard Hartley},
booktitle={International Conference on Learning Representations},
year={2021},
}

@article{Grigorescu2020,
author = {Grigorescu, Sorin and Trasnea, Bogdan and Cocias, Tiberiu and Macesanu, Gigel},
title = {A survey of deep learning techniques for autonomous driving},
journal = {Journal of Field Robotics},
volume = {37},
number = {3},
pages = {362-386},
year = {2020}
}

@article{Abdallah2016,
title = {Fraud detection system: A survey},
journal = {Journal of Network and Computer Applications},
volume = {68},
pages = {90-113},
year = {2016},
issn = {1084-8045},
author = {Aisha Abdallah and Mohd Aizaini Maarof and Anazida Zainal},
}

@article{Elter2007,
title = {The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process},
author = {Elter, M and Schulz-Wendtland, R and Wittenberg, T},
journal = {Medical Physics},
number = {11},
volume = {34},
year = {2007},
}

@techreport{Putten2000,
author = {Peter van der Putten and Maarten van Someren},
year = {2000-2009},
title = {CoIL Challenge 2000: The Insurance Company Case},
institution = {Sentient Machine Research, Amsterdam and Leiden Institute of Advanced Computer Science},
}

@Article{Henzi2022,
  author={Alexander Henzi and Alexandre Mösching and Lutz Dümbgen},
  title={{Accelerating the Pool-Adjacent-Violators Algorithm for Isotonic Distributional Regression}},
  journal={Methodology and Computing in Applied Probability},
  year=2022,
  volume={24},
  number={4},
  pages={2633-2645},
}

@misc{Dua2017,
    author = "Dua, Dheeru and Graff, Casey",
    year = "2017",
    title = "{UCI} Machine Learning Repository",
    url = "http://archive.ics.uci.edu/ml",
    institution = "University of California, Irvine, School of Information and Computer Sciences"
}

@inproceedings{McAllister2017,
  author = {Rowan McAllister and Yarin Gal and Alex Kendall and Mark van der Wilk and Amar Shah and Roberto Cipolla and Adrian Weller},
  title = {Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning},
  booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, {IJCAI-17}},
  pages = {4745--4753},
  year = {2017},
}

@article{Topol2019,
author = {Eric Topol},
title = {High-performance medicine: the convergence of human and artificial intelligence},
journal = {Nature Medicine},
volume = {25},
pages = {44--56},
year = {2019},
}

@inproceedings{Tax2021,
  title={Machine learning for fraud detection in e-Commerce: A research agenda},
  author={Tax, Niek and de Vries, Kees Jan and de Jong, Mathijs and Dosoula, Nikoleta and van den Akker, Bram and Smith, Jon and Thuong, Olivier and Bernardi, Lucas},
  booktitle={Proceedings of the KDD International Workshop on Deployable Machine Learning for Security Defense (MLHat)},
  pages={30--54},
  year={2021},
  organization={Springer}
}

@article{Yang2022,
  title={Click-through rate prediction in online advertising: A literature review},
  author={Yang, Yanwu and Zhai, Panyu},
  journal={Information Processing \& Management},
  volume={59},
  number={2},
  pages={102853},
  year={2022},
  publisher={Elsevier}
}

@article{Storkey2009,
  title={When training and test sets are different: characterizing learning transfer},
  author={Storkey, Amos and others},
  journal={Dataset shift in machine learning},
  volume={30},
  pages={3--28},
  year={2009},
  publisher={Citeseer}
}

@article{Dimitriadis2021,
author = {Timo Dimitriadis  and Tilmann Gneiting  and Alexander I. Jordan },
title = {Stable reliability diagrams for probabilistic classifiers},
journal = {Proceedings of the National Academy of Sciences},
volume = {118},
number = {8},
year = {2021},
}

@article{Leeuw2009,
 title={Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods},
 volume={32},
 number={5},
 journal={Journal of Statistical Software},
 author={Jan de Leeuw and Kurt Hornik and Patrick Mair},
 year={2009},
 pages={1–24}
}

@article{Tibshirani2011,
author = {Ryan J. Tibshirani and Holger Hoefling and Robert Tibshirani},
title = {Nearly-Isotonic Regression},
journal = {Technometrics},
volume = {53},
number = {1},
pages = {54-61},
year  = {2011},
}

@article{Lemaitre2017,
author  = {Guillaume Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
journal = {Journal of Machine Learning Research},
year    = {2017},
volume  = {18},
number  = {17},
pages   = {1-5},
}

@inproceedings{Widmann2019,
 author = {Widmann, David and Lindsten, Fredrik and Zachariah, Dave},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Calibration tests in multi-class classification: A unifying framework},
 volume = {32},
 year = {2019}
}

@inproceedings{Vaicenavicius2019,
  title={Evaluating Model Calibration in Classification},
  author={Juozas Vaicenavicius and David Widmann and Carl R. Andersson and Fredrik Lindsten and Jacob Roll and Thomas Bo Sch{\"o}n},
  booktitle={Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics},
  year={2019}
}

@article{Gneiting2007,
author = {Gneiting, Tilmann and Balabdaoui, Fadoua and Raftery, Adrian E.},
title = {Probabilistic forecasts, calibration and sharpness},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
volume = {69},
number = {2},
pages = {243-268},
year = {2007}
}

@article{Kull2017,
author = {Meelis Kull and Telmo M. Silva Filho and Peter Flach},
title = {{Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration}},
volume = {11},
journal = {Electronic Journal of Statistics},
number = {2},
pages = {5052 -- 5080},
year = {2017},
}

@inproceedings{Zadrozny2001,
author = {Zadrozny, Bianca and Elkan, Charles},
title = {Obtaining Calibrated Probability Estimates from Decision Trees and Naive Bayesian Classifiers},
year = {2001},
booktitle = {Proceedings of the Eighteenth International Conference on Machine Learning},
pages = {609–616},
numpages = {8},
}

@inproceedings{Zadrozny2002,
author = {Zadrozny, Bianca and Elkan, Charles},
title = {Transforming Classifier Scores into Accurate Multiclass Probability Estimates},
year = {2002},
booktitle = {Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {694–699},
numpages = {6},
}

@article{Platt1999,
  author = {John C. Platt},
  journal = {Advances in Large Margin Classifiers},
  number = {3},
  title = {Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods},
  volume = {10},
  year = {1999}
}

@article {Brier1950,
    author = "Glen W. Brier",
    title = "Verification of Forecasts Expressed in Terms of Probability",
    journal = "Monthly Weather Review",
    year = "1950",
    volume = "78",
    number = "1",
    pages = "1 - 3",
}

@inproceedings{Kumar2019,
 author = {Kumar, Ananya and Liang, Percy S and Ma, Tengyu},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Verified Uncertainty Calibration},
 volume = {32},
 year = {2019}
}

@article{Dawid1981,
author = {Philip Dawid},
title = {The Well-Calibrated Bayesian},
journal = {Journal of the American Statistical Association},
volume = {77},
number = {379},
pages = {605-610},
year  = {1982},
}

@inproceedings{Niculescu-Mizil2005,
author = {Niculescu-Mizil, Alexandru and Caruana, Rich},
title = {Predicting Good Probabilities with Supervised Learning},
year = {2005},
booktitle = {Proceedings of the 22nd International Conference on Machine Learning},
pages = {625–632},
numpages = {8},
}

@article{Degroot1983,
  title={The Comparison and Evaluation of Forecasters.},
  author={Morris H. Degroot and Stephen E. Fienberg},
  journal={The Statistician},
  year={1983},
  volume={32},
  pages={12-22}
}

@InProceedings{Guo2017,
  title = 	 {On Calibration of Modern Neural Networks},
  author =       {Chuan Guo and Geoff Pleiss and Yu Sun and Kilian Q. Weinberger},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
  pages = 	 {1321--1330},
  year = 	 {2017},
  volume = 	 {70},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR},
}

@InProceedings{Nixon2019,
author = {Nixon, Jeremy and Dusenberry, Michael W. and Zhang, Linchuan and Jerfel, Ghassen and Tran, Dustin},
title = {Measuring Calibration in Deep Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}

@inproceedings{Kull2019,
 author = {Kull, Meelis and Perello Nieto, Miquel and K\"{a}ngsepp, Markus and Silva Filho, Telmo and Song, Hao and Flach, Peter},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration},
 volume = {32},
 year = {2019}
}

@inproceedings{Minderer2021,
 title={Revisiting the Calibration of Modern Neural Networks},
 author={Matthias Minderer and Josip Djolonga and Rob Romijnders and Frances Ann Hubis and Xiaohua Zhai and Neil Houlsby and Dustin Tran and Mario Lucic},
 booktitle={Advances in Neural Information Processing Systems},
 year={2021},
}

@inproceedings{Naeini2015,
 author = {Naeini, M. P. and Cooper, G. F. and Hauskrecht, M.},
 booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
 title = {Obtaining Well Calibrated Probabilities Using Bayesian Binning},
 year = {2015},
 pages={2901–-2907}
}

@INPROCEEDINGS{Naeini2016,
  author={Naeini, Mahdi Pakdaman and Cooper, Gregory F.},
  booktitle={Proceedings of the Sixteenth International Conference on Data Mining}, 
  title={Binary Classifier Calibration Using an Ensemble of Near Isotonic Regression Models}, 
  year={2016},
  pages={360-369},
}

@article{Brocker2009,
 author = {Bröcker, Jochen},
 title = {Reliability, sufficiency, and the decomposition of proper scores},
 journal = {Quarterly Journal of the Royal Meteorological Society},
 volume = {135},
 number = {643},
 pages = {1512-1519},
 year = {2009}
}

@InProceedings{Allikivi2020,
author={Allikivi, Mari-Liis and Kull, Meelis},
title={{Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification}},
booktitle={{Machine Learning and Knowledge Discovery in Databases}},
year={2020},
pages={103--120},
}

@inproceedings{Li2015,
  title={Click-through prediction for advertising in twitter timeline},
  author={Li, Cheng and Lu, Yue and Mei, Qiaozhu and Wang, Dong and Pandey, Sandeep},
  booktitle={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={1959--1968},
  year={2015}
}


@article{wallace2014improving,
  title={Improving class probability estimates for imbalanced data},
  author={Wallace, Byron C and Dahabreh, Issa J},
  journal={Knowledge and Information Systems},
  volume={41},
  number={1},
  pages={33--52},
  year={2014},
  publisher={Springer}
}


%%%%%% NEXT 4 PAPERS ARE EXAMPLES OF PAPER ON PROBABILITY CALIBRATION UNDER CLASS IMBALANCE THAT EVALUATE ON ECE
@article{huang2020experimental,
  title={An experimental investigation of calibration techniques for imbalanced data},
  author={Huang, Lanlan and Zhao, Junkai and Zhu, Bing and Chen, Hao and Broucke, Seppe Vanden},
  journal={IEEE Access},
  volume={8},
  pages={127343--127352},
  year={2020},
  publisher={IEEE}
}

@article{fernando2021dynamically,
  title={Dynamically weighted balanced loss: class imbalanced learning and confidence calibration of deep neural networks},
  author={Fernando, K Ruwani M and Tsokos, Chris P},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  volume={33},
  number={7},
  pages={2940--2951},
  year={2021},
  publisher={IEEE}
}

@article{larrazabal2021maximum,
  title={Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation},
  author={Larrazabal, Agostina and Martinez, Cesar and Dolz, Jose and Ferrante, Enzo},
  journal={arXiv preprint arXiv:2112.12218},
  year={2021}
}

@article{rajaraman2022deep,
  title={Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks},
  author={Rajaraman, Sivaramakrishnan and Ganesan, Prasanth and Antani, Sameer},
  journal={Plos one},
  volume={17},
  number={1},
  pages={e0262838},
  year={2022},
  publisher={Public Library of Science San Francisco, CA USA}
}
%%%%%%%%%