book.bib

@Book{xie2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {http://yihui.org/knitr/},
}

@Manual{rlang,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2018},
  url = {https://www.R-project.org/},
}

@Book{EMA,
  author = {Przemyslaw Biecek and Tomasz Burzykowski},
  title = {{Explanatory Model Analysis}},
  publisher = {Chapman and Hall/CRC, New York},
  year = {2021},
  isbn = {9780367135591},
  url = {https://pbiecek.github.io/ema/},
}

@article{li_et_al_2020, 
  title = {Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical      variables}, 
  volume = {8}, 
  DOI = {10.7717/peerj.10337}, 
  journal = {PeerJ}, 
  author = {Li, Xiaoran and Ge, Peilin and Zhu, Jocelyn and Li, Haifang and Graham, James and Singer, Adam and Richman, Paul S. and Duong, Tim Q.}, 
  year = {2020}
} 

@article{yan_et_al_2020, 
  title = {An interpretable mortality prediction model for COVID-19 patients}, 
  volume = {2}, 
  DOI = {10.1038/s42256-020-0180-7}, 
  number = {5}, 
  journal = {Nature Machine Intelligence}, 
  author = {Yan, Li and Zhang, Hai-Tao and Goncalves, Jorge and Xiao, Yang and Wang, Maolin and Guo, Yuqi and Sun, Chuan and Tang, Xiuchuan and Jing, Liang and Zhang, Mingyang and et al.}, 
  year = {2020}, 
  pages = {283–288}
} 

@article{scikitlearn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}

@article{dalex,
  title={{dalex: Responsible Machine Learning with Interactive
          Explainability and Fairness in Python}},
  author={Hubert Baniecki and Wojciech Kretowicz and Piotr Piatyszek
          and Jakub Wisniewski and Przemyslaw Biecek},
  year={2020},
  journal={arXiv:2012.14406},
  url={https://arxiv.org/abs/2012.14406}
}

@article{xai1-breakdown,
  author = {Mateusz Staniak and Przemysław Biecek},
  title = {Explanations of model predictions with live and breakDown packages},
  year = {2018}}

@incollection{xai1-shapleyvalues,
    title        = {A unified approach to interpreting model predictions},
    author       = {Lundberg, Scott M and Lee, Su-In},
    year         = 2017,
    booktitle    = {Advances in Neural Information Processing Systems 30},
    publisher    = {Curran Associates},
    address      = {Montreal}, 
    pages        = {4765--4774},
    url          = {http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf},
    editor       = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett}
}

@inproceedings{xai1-lime,
  author    = {Marco Tulio Ribeiro and
               Sameer Singh and
               Carlos Guestrin},
  title     = {"{Why should I trust you}?": explaining the predictions of any classifier},
  booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} International Conference on
               Knowledge Discovery and Data Mining, KDD San Francisco, CA},
  pages     = {1135--1144},
  year      = {2016},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY}
}

@article{xai1-ice,
  title = {Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation},
  author = {Alex Goldstein and Adam Kapelner and Justin Bleich and Emil Pitkin},
  year = {2014}}

@article{xai1-vip,
  author = {Aaron Fisher and Cynthia Rudin and Francesca Dominici},
  title = {All models are Wrong, but many are Useful: Learning a variable's importance by studying an entire class of prediction models simultaneously},
  journal = {Journal of Machine Learning Research},
  year = {2019},
  volume = {20},
  number = {177},
  pages = {1-81},
  url = {http://jmlr.org/papers/v20/18-760.html}
  }

@article{xai1-pdp,
    title        = {Greedy Function Approximation: A Gradient Boosting Machine},
    author       = {Jerome H. Friedman},
    year         = 2000,
    journal      = {Annals of Statistics},
    volume       = 29,
    pages        = {1189--1232}
}

@article{xai1-ale,
  title        = {{Visualizing the effects of predictor variables in black box supervised learning models}},
  author={Daniel W. Apley and Jingyu Zhu},
  journal={Journal of the Royal Statistical Society Series B},
  year=2020,
  volume={82},
  number={4},
  pages={1059--1086},
  month={September},
  keywords={},
  doi={10.1111/rssb.12377}
}

@manual{EUGDPR,
    title        = {{The EU General Data Protection Regulation (GDPR) is the most important change in data privacy regulation in 20 years}},
    author       = {{GDPR}},
    year         = 2018,
    url          = {https://eugdpr.org/}
}

@article{1-5-hotels-automated-ml-system,
title = {An Automated Machine Learning Based Decision Support System to Predict Hotel Booking Cancellations},
author = {Nuno Antonio and Ana de Almeida and Luis Nunes},
journal = {Data Science Journal},
volume = {18},
publisher = {Ubiquity Press},
number = {1},
year = {2019},
pages = {1--20},
doi = {10.5334/dsj-2019-032}
}

@article{1-5-hotels-predicting-to-decrease-uncertainty-increase-revenue,
title = {Predicting hotel booking cancellations to decrease uncertainty and increase revenue},
author = {Nuno Antonio and Ana de Almeida and Luis Nunes},
journal = {Tourism & Management Studies},
volume = {13},
publisher = {University of the Algarve - ESGHT - CIEO},
number = {2},
year = {2017},
pages = {25--39},
doi = {10.18089/tms.2017.13203}
}

@article{1-5-hotels-cancellation-policies-shift,
title = {A paradigm shift in revenue management? The new landscape of hotel cancellation policies},
author = {Arash Riasi and Zvi Schwartz and Chih-Chien Chen},
journal = {Journal of Revenue and Pricing Management},
volume = {18},
publisher = {Palgrave Macmillan},
number = {6},
year = {2019},
pages = {434--440},
doi = {10.1057/s41272-019-00189-3}
}

@article{1-5-hotels-modelling-cancellation-behaviour,
title = {Modelling the cancellation behaviour of hotel guests},
author = {Martin Falk and Markku Vieru},
journal = {International Journal of Contemporary Hospitality Management},
volume = {30},
publisher = {Emerald Publishing Limited},
number = {10},
year = {2018},
pages = {3100--3116},
doi = {10.1108/ijchm-08-2017-0509}
}

@inproceedings{1-5-hotels-prediction-using-crisp-dm,
title = {Prediction of Hotel Booking Cancellation using CRISP-DM}, 
author = {Z. A. {Andriawan} and S. R. {Purnama} and A. S. {Darmawan} and  {Ricko} and A. {Wibowo} and A. {Sugiharto} and F. {Wijayanto}},
booktitle = {2020 4th International Conference on Informatics and Computational Sciences (ICICoS)}, 
year = {2020},
pages = {1--6},
doi = {10.1109/ICICoS51170.2020.9299011}
}

@article{1-5-hotels-efficient-forecasting,
title = {Using machine learning and big data for efficient forecasting of hotel booking cancellations},
author = {Agustín J Sánchez-Medina and Eleazar C-Sánchez},
journal = {International Journal of Hospitality Management},
volume = {89},
publisher = {Elsevier Ltd},
year = {2020},
pages = {102546},
doi = {10.1016/j.ijhm.2020.102546}
}

@article{1-5-cancellations-policies-study,
title = {Hotel Cancelation Policies, Distributive and Procedural Fairness, and Consumer Patronage: A Study of the Lodging Industry},
author = {Scott J. Smith and H.G. Parsa and Milos Bujisic and Jean-Pierre van der Rest},
journal = {Journal of Travel & Tourism Marketing},
volume = {32},
publisher= {Taylor & Francis (Routledge)},
year = {2015},
pages = {886--906},
doi = {10.1080/10548408.2015.1063864}
}

@article{5-2-china,
author={Yan, Li
and Zhang, Hai-Tao
and Goncalves, Jorge
and Xiao, Yang
and Wang, Maolin
and Guo, Yuqi
and Sun, Chuan
and Tang, Xiuchuan
and Jing, Liang
and Zhang, Mingyang
and Huang, Xiang
and Xiao, Ying
and Cao, Haosen
and Chen, Yanyan
and Ren, Tongxin
and Wang, Fang
and Xiao, Yaru
and Huang, Sufang
and Tan, Xi
and Huang, Niannian
and Jiao, Bo
and Cheng, Cheng
and Zhang, Yong
and Luo, Ailin
and Mombaerts, Laurent
and Jin, Junyang
and Cao, Zhiguo
and Li, Shusheng
and Xu, Hui
and Yuan, Ye},
title={An interpretable mortality prediction model for COVID-19 patients},
journal={Nature Machine Intelligence},
year={2020},
month={May},
day={01},
volume={2},
number={5},
pages={283-288},
issn={2522-5839},
doi={10.1038/s42256-020-0180-7},
url={https://doi.org/10.1038/s42256-020-0180-7}
}

@article{5-2-newyork,
author={Barish, Matthew
and Bolourani, Siavash
and Lau, Lawrence F.
and Shah, Sareen
and Zanos, Theodoros P.},
title={External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19},
journal={Nature Machine Intelligence},
year={2021},
month={Jan},
day={01},
volume={3},
number={1},
pages={25-27},
issn={2522-5839},
doi={10.1038/s42256-020-00254-2},
url={https://doi.org/10.1038/s42256-020-00254-2}
}

@article{5-2-netherlands,
author={Quanjel, Marian J. R.
and van Holten, Thijs C.
and Gunst-van der Vliet, Pieternel C.
and Wielaard, Jette
and Karakaya, Bekir
and S{\"o}hne, Maaike
and Moeniralam, Hazra S.
and Grutters, Jan C.},
title={Replication of a mortality prediction model in Dutch patients with COVID-19},
journal={Nature Machine Intelligence},
year={2021},
month={Jan},
day={01},
volume={3},
number={1},
pages={23-24},
issn={2522-5839},
doi={10.1038/s42256-020-00253-3},
url={https://doi.org/10.1038/s42256-020-00253-3}
}


@article{5-2-france,
author={Dupuis, C.
and De Montmollin, E.
and Neuville, M.
and Mourvillier, B.
and Ruckly, S.
and Timsit, J. F.},
title={Limited applicability of a COVID-19 specific mortality prediction rule to the intensive care setting},
journal={Nature Machine Intelligence},
year={2021},
month={Jan},
day={01},
volume={3},
number={1},
pages={20-22},
issn={2522-5839},
doi={10.1038/s42256-020-00252-4},
url={https://doi.org/10.1038/s42256-020-00252-4}
}

@article{5-2-dalex,
  title={dalex: Responsible Machine Learning with Interactive
         Explainability and Fairness in Python},
  author={Hubert Baniecki and Wojciech Kretowicz and Piotr Piatyszek
          and Jakub Wisniewski and Przemyslaw Biecek},
  year={2020},
  eprint={2012.14406},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2012.14406}
}

@techreport{5-2-bird2020fairlearn,
    author = {Bird, Sarah and Dud{\'i}k, Miro and Edgar, Richard and Horn, Brandon and Lutz, Roman and Milan, Vanessa and Sameki, Mehrnoosh and Wallach, Hanna and Walker, Kathleen},
    title = {Fairlearn: A toolkit for assessing and improving fairness in {AI}},
    institution = {Microsoft},
    year = {2020},
    month = {May},
    url = "https://www.microsoft.com/en-us/research/publication/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai/",
    number = {MSR-TR-2020-32},
}

@article{6-0-breiman2001statistical,
  title={Statistical modeling: The two cultures (with comments and a rejoinder by the author)},
  author={Breiman, Leo and others},
  journal={Statistical science},
  volume={16},
  number={3},
  pages={199--231},
  year={2001},
  publisher={Institute of Mathematical Statistics}
}

@article{6-0-rashomon-intro,
  title={A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning},
  author={Semenova, Lesia and Rudin, Cynthia and Parr, Ronald},
  journal={arXiv preprint arXiv:1908.01755},
  year={2019}
}

@article{6-0-rashomon-variable-importance,
  title={All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.},
  author={Fisher, Aaron and Rudin, Cynthia and Dominici, Francesca},
  journal={Journal of Machine Learning Research},
  volume={20},
  number={177},
  pages={1--81},
  year={2019}
}

@article{6-0-rudin-challenges,
  title={Interpretable machine learning: Fundamental principles and 10 grand challenges},
  author={Rudin, Cynthia and Chen, Chaofan and Chen, Zhi and Huang, Haiyang and Semenova, Lesia and Zhong, Chudi},
  journal={arXiv preprint arXiv:2103.11251},
  year={2021}
}

@article{6-0-rashomon-variable-importance-cloud,
  title={Exploring the cloud of variable importance for the set of all good models},
  author={Dong, Jiayun and Rudin, Cynthia},
  journal={Nature Machine Intelligence},
  volume={2},
  number={12},
  pages={810--824},
  year={2020},
  publisher={Nature Publishing Group}
}

@article{6-0-mimic,
  title={MIMIC-III, a freely accessible critical care database},
  author={Johnson, Alistair EW and Pollard, Tom J and Shen, Lu and Li-Wei, H Lehman and Feng, Mengling and Ghassemi, Mohammad and Moody, Benjamin and Szolovits, Peter and Celi, Leo Anthony and Mark, Roger G},
  journal={Scientific data},
  volume={3},
  number={1},
  pages={1--9},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{6-0-purushotham2018benchmarking,
  title={Benchmarking deep learning models on large healthcare datasets},
  author={Purushotham, Sanjay and Meng, Chuizheng and Che, Zhengping and Liu, Yan},
  journal={Journal of biomedical informatics},
  volume={83},
  pages={112--134},
  year={2018},
  publisher={Elsevier}
}

@article{6-0-tang2018predictive,
  title={Predictive modeling in urgent care: a comparative study of machine learning approaches},
  author={Tang, Fengyi and Xiao, Cao and Wang, Fei and Zhou, Jiayu},
  journal={Jamia Open},
  volume={1},
  number={1},
  pages={87--98},
  year={2018},
  publisher={Oxford University Press}
}

@InProceedings{6-0-johnson17reproducibility,
  title = 	 {Reproducibility in critical care: a mortality prediction case study},
  author = 	 {Alistair E. W. Johnson and Tom J. Pollard and Roger G. Mark},
  booktitle = 	 {Proceedings of the 2nd Machine Learning for Healthcare Conference},
  pages = 	 {361--376},
  year = 	 {2017},
  editor = 	 {Finale Doshi-Velez and Jim Fackler and David Kale and Rajesh Ranganath and Byron Wallace and Jenna Wiens},
  volume = 	 {68},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Boston, Massachusetts},
  month = 	 {18--19 Aug},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v68/johnson17a/johnson17a.pdf},
  url = 	 {http://proceedings.mlr.press/v68/johnson17a.html}
}

@article{6-1-beams-system,  
  author={Das, Subhajit and Cashman, Dylan and Chang, Remco and Endert, Alex},
  journal={IEEE Computer Graphics and Applications},   
  title={BEAMES: Interactive Multimodel Steering, Selection, and Inspection for Regression Tasks},   
  year={2019},  
  volume={39},  
  number={5},  
  pages={20-32},  
  doi={10.1109/MCG.2019.2922592}
}

@inproceedings{4-1-RMDL, 
  title={Rmdl: Random multimodel deep learning for classification}, 
  author={Kowsari, Kamran and Heidarysafa, Mojtaba and Brown, Donald E and Meimandi, Kiana Jafari and Barnes, Laura E}, 
  booktitle={Proceedings of the 2nd International Conference on Information System and Data Mining}, 
  pages={19--28}, 
  year={2018} 
} 

@inproceedings{4-1-facial-expression-recognition, 
  author = {Barsoum, Emad and Zhang, Cha and Ferrer, Cristian Canton and Zhang, Zhengyou}, 
  title = {Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution}, 
  year = {2016}, 
  isbn = {9781450345569}, 
  publisher = {Association for Computing Machinery}, 
  address = {New York, NY, USA}, 
  url = {https://doi.org/10.1145/2993148.2993165}, 
  doi = {10.1145/2993148.2993165}, 
  booktitle = {Proceedings of the 18th ACM International Conference on Multimodal Interaction}, 
  pages = {279–283}, 
  numpages = {5}, 
  keywords = {Crowd sourcing, Annotation, Emotion recognition, Facial Expression Recognition, Convolutional Neural Network}, 
  location = {Tokyo, Japan}, 
  series = {ICMI '16} 
} 

  

@Article{4-1-axon-deep-seg, 
  author={Zaimi Aldo, Wabartha Maxime and Herman, Victor and Antonsanti, Pierre-Louis and Perone, Christian S. and Cohen-Adad, Julien}, 
  title={AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks}, 
  journal={Scientific Reports}, 
  year={2018}, 
  month={Feb}, 
  day={28}, 
  volume={8}, 
  number={1}, 
  pages={3816}, 
  issn={2045-2322}, 
  doi={10.1038/s41598-018-22181-4}, 
  url={https://doi.org/10.1038/s41598-018-22181-4} 
} 

@article{4-2-ara,  
title = {ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning}, 
author = {Łukasz Rączkowski, Marcin Możejko, Joanna Zambonelli, Ewa Szczurek}, 
journal = {Springer Nature}, 
volume = {14}, 
publisher = {bioRxiv}, 
year = {2019}, 
pages = {1-11}, 
doi = {10.1038/s41598-019-50587-1} 
} 

 

@article{4-4-UNet, 
title={UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation}, 
author={Zhou, Zongwei and Siddiquee, Md Mahfuzur Rahman and Tajbakhsh, Nima and Liang, Jianming}, 
journal={IEEE Transactions on Medical Imaging}, 
year={2019},  
publisher={IEEE}
} 

@article{4-5-AdversarialAttacks, 
  title={Adversarial attacks against medical deep learning systems}, 
  author={Finlayson, Samuel G and Chung, Hyung Won and Kohane, Isaac S and Beam, Andrew L}, 
  journal={arXiv preprint arXiv:1804.05296}, 
  year={2018} 
} 

 

@article{4-5-ReproducibilityInML, 
  author    = {Joelle Pineau and Philippe Vincent{-}Lamarre and Koustuv Sinha and Vincent Larivi{\`{e}}re and 
               Alina Beygelzimer and Florence d'Alch{\'{e}}{-}Buc and Emily B. Fox and Hugo Larochelle}, 
  title     = {Improving Reproducibility in Machine Learning Research {(A} Report from the NeurIPS 2019 Reproducibility Program)}, 
  journal   = {CoRR}, 
  volume    = {abs/2003.12206}, 
  year      = {2020}, 
  url       = {https://arxiv.org/abs/2003.12206}, 
  archivePrefix = {arXiv}, 
  eprint    = {2003.12206}, 
  timestamp = {Wed, 01 Apr 2020 17:39:12 +0200}, 
  biburl    = {https://dblp.org/rec/journals/corr/abs-2003-12206.bib}, 
  bibsource = {dblp computer science bibliography, https://dblp.org} 
} 

@book{4-5-LogicOfScientificDisc, 
  title={The logic of scientific discovery}, 
  author={Popper, Karl}, 
  pages={66},  
  year={1959}, 
  publisher={Routledge} 
} 

@book{4-5-DesignExperiments, 
  title={The Design of Experiments}, 
  author={Fisher, Ronald}, 
  pages={14},  
  year={1935}, 
  publisher={Oliver and Boyd} 
} 

@article {4-5-ReproducibleScience, 
author = {Casadevall, Arturo and Fang, Ferric C.}, 
title = {Reproducible Science}, 
volume = {78}, 
number = {12}, 
pages = {4972--4975}, 
year = {2010}, 
doi = {10.1128/IAI.00908-10}, 
publisher = {American Society for Microbiology Journals}, 
issn = {0019-9567}, 
URL = {https://iai.asm.org/content/78/12/4972}, 
eprint = {https://iai.asm.org/content/78/12/4972.full.pdf}, 
journal = {Infection and Immunity} 
} 
@article{5-3-chinese_et_al, 
  title = {An interpretable mortality prediction model for COVID-19 patients}, 
  volume = {2}, 
  DOI = {10.1038/s42256-020-0180-7}, 
  number = {5}, 
  journal = {Nature Machine Intelligence}, 
  author = {Yan, Li and Zhang, Hai-Tao and Goncalves, Jorge and Xiao, Yang and Wang, Maolin and Guo, Yuqi and Sun, Chuan and Tang, Xiuchuan and Jing, Liang and Zhang, Mingyang and et al.}, 
  year = {2020}, 
  pages = {283–288}
}

@article{5-3-mexican_et_al, 
  title = {Predicting Mortality Due to SARS-CoV-2: A Mechanistic Score Relating Obesity and Diabetes to COVID-19 Outcomes in Mexico}, 
  volume = {105}, 
  journal = {The Journal of Clinical Endocrinology & Metabolism}, 
  author = {Omar Yaxmehen Bello-Chavolla, Jessica Paola Bahena-López, Neftali Eduardo Antonio-Villa, Arsenio Vargas-Vázquez, Armando González-Díaz, Alejandro Márquez-Salinas, Carlos A Fermín-Martínez, J Jesús Naveja, Carlos A Aguilar-Salinas and et al.}, 
  year = {2020}, 
  pages = {2752–2761}
}