biblio.bib

% Encoding: UTF-8

% XAI

@Book{molnar2019,
	title      = {Interpretable Machine Learning},
	author     = {Christoph Molnar},
	publisher  = {\url{https://christophm.github.io/interpretable-ml-book}},
	year       = {2019},
	subtitle   = {A Guide for Making Black Box Models Explainable}
}

@Book{biecek2021,
	author = {Przemyslaw Biecek and Tomasz Burzykowski},
	title = {{Explanatory Model Analysis}},
	publisher = {Chapman and Hall/CRC, New York},
	year = {2021},
	url = {https://pbiecek.github.io/ema/},
	doi = {10.1201/9780429027192}
}

@misc{mayer2020,
    title={Peeking into the Black Box: An Actuarial Case Study for Interpretable Machine Learning},
    author={Michael Mayer and Christian Lorentzen},
    year={2020},
    doi = {http://dx.doi.org/10.2139/ssrn.3595944}
}

@Misc{mayer2021ml,
	title      = {Introduction to Machine Learning},
	author     = {Michael Mayer},
	publisher  = {\url{https://github.com/mayer79/ml_lecture}},
	year       = {2021}}
	

@article{mayer2022,
  AUTHOR = {Mayer, Michael and Bourassa, Steven C. and Hoesli, Martin and Scognamiglio, Donato},
  TITLE = {Machine Learning Applications to Land and Structure Valuation},
  JOURNAL = {Journal of Risk and Financial Management},
  VOLUME = {15},
  YEAR = {2022},
  NUMBER = {5},
  ARTICLE-NUMBER = {193},
  URL = {https://www.mdpi.com/1911-8074/15/5/193},
  DOI = {10.3390/jrfm15050193}
}


@article{fissler2022,
  doi = {10.48550/ARXIV.2202.12780},
  url = {https://arxiv.org/abs/2202.12780},
  author = {Fissler, Tobias and Lorentzen, Christian and Mayer, Michael},
  title = {Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}


@article{goldstein2015,
	author = {Alex Goldstein and Adam Kapelner and Justin Bleich and Emil Pitkin},
	title = {Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation},
	journal = {Journal of Computational and Graphical Statistics},
	volume = {24},
	number = {1},
	pages = {44-65},
	year  = {2015},
	publisher = {Taylor & Francis},
	doi = {10.1080/10618600.2014.907095},
}

@article{friedman2008,
	author = {Jerome H. Friedman and Bogdan E. Popescu},
	title = {Predictive Learning via Rule Ensembles},
	journal = {The Annals of Applied Statistics},
	doi = {10.1214/07-AOAS148},
	url = {http://www.jstor.org/stable/30245114},
	number = {3},
	pages = {916--954},
	publisher = {Institute of Mathematical Statistics},
	volume = {2},
	year = {2008},
}

@misc{fisher2018,
    title={All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously},
    author={Aaron Fisher and Cynthia Rudin and Francesca Dominici},
    year={2018},
    url = {https://arxiv.org/abs/1801.01489},
    primaryClass={stat.ME}
}

@misc{apley2016,
    title={Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models},
    author={Daniel W. Apley and Jingyu Zhu},
    year={2016},
    url = {https://arxiv.org/abs/1612.08468},
    doi = {10.48550/arXiv.1612.08468}
}

@inproceedings{craven95,
  author = {Craven, Mark W. and Shavlik, Jude W.},
  title = {Extracting Tree-Structured Representations of Trained Networks},
  year = {1995},
  publisher = {MIT Press},
  address = {Cambridge, MA, USA},
  booktitle = {Proceedings of the 8th International Conference on Neural Information Processing Systems},
  pages = {24–30},
  numpages = {7},
  location = {Denver, Colorado},
  series = {NIPS'95}
}

@incollection{shapley1953,
	author = {Lloyd S. Shapley},
	title = {A Value for n-Person Games},
	doi = {10.1515/9781400881970-018},
	year = {1953},
	month = {dec},
	publisher = {Princeton University Press},
	pages = {307--318},
	editor = {Harold William Kuhn and Albert William Tucker},
	booktitle = {Contributions to the Theory of Games ({AM}-28),  Volume {II}}
}

@incollection{lundberg2017,
	title = {A Unified Approach to Interpreting Model Predictions},
	author = {Lundberg, Scott M. and Lee, Su-In},
	booktitle = {Advances in Neural Information Processing Systems 30},
	editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
	pages = {4765--4774},
	year = {2017},
	publisher = {Curran Associates, Inc.},
	url = {https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}
}

@article{lundberg2020,
  title={From local explanations to global understanding with explainable AI for trees},
  author={Lundberg, Scott M. and Erion, Gabriel and Chen, Hugh and DeGrave, Alex and Prutkin, Jordan M. and Nair, Bala and Katz, Ronit and Himmelfarb, Jonathan and Bansal, Nisha and Lee, Su-In},
  journal={Nature Machine Intelligence},
  volume={2},
  number={1},
  pages={2522-5839},
  year={2020},
  publisher={Nature Publishing Group}
}

@article{lipovetsky2001,
  author = {Lipovetsky, Stan and Conklin, Michael},
  title = {Analysis of regression in game theory approach},
  journal = {Applied Stochastic Models in Business and Industry},
  volume = {17},
  number = {4},
  pages = {319-330},
  keywords = {co-operative games, Shapley Value, multicollinearity, regressors net effects},
  doi = {https://doi.org/10.1002/asmb.446},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asmb.446},
  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/asmb.446},
  abstract = {Abstract Working with multiple regression analysis a researcher usually wants to know a comparative importance of predictors in the model. However, the analysis can be made difficult because of multicollinearity among regressors, which produces biased coefficients and negative inputs to multiple determination from presum ably useful regressors. To solve this problem we apply a tool from the co-operative games theory, the Shapley Value imputation. We demonstrate the theoretical and practical advantages of the Shapley Value and show that it provides consistent results in the presence of multicollinearity. Copyright © 2001 John Wiley \& Sons, Ltd.},
  year = {2001}
}

@article{strumbelj2010,
  title={An Efficient Explanation of Individual Classifications using Game Theory},
  author={\v{S}trumbelj, Erik and Kononenko, Igor},
  journal={J. Mach. Learn. Res.},
  year={2010},
  volume={11},
  pages={1-18}
}

@article{strumbelj2014,
  author = {\v{S}trumbelj, Erik and Kononenko, Igor},
  title = {Explaining Prediction Models and Individual Predictions with Feature Contributions},
  year = {2014},
  issue_date = {December 2014},
  publisher = {Springer-Verlag},
  address = {Berlin, Heidelberg},
  volume = {41},
  number = {3},
  issn = {0219-1377},
  url = {https://doi.org/10.1007/s10115-013-0679-x},
  doi = {10.1007/s10115-013-0679-x},
  abstract = {We present a sensitivity analysis-based method for explaining prediction models that can be applied to any type of classification or regression model. Its advantage over existing general methods is that all subsets of input features are perturbed, so interactions and redundancies between features are taken into account. Furthermore, when explaining an additive model, the method is equivalent to commonly used additive model-specific methods. We illustrate the method's usefulness with examples from artificial and real-world data sets and an empirical analysis of running times. Results from a controlled experiment with 122 participants suggest that the method's explanations improved the participants' understanding of the model.},
  journal = {Knowl. Inf. Syst.},
  month = {dec},
  pages = {647–665},
  numpages = {19},
  keywords = {Visualization, Decision support, Knowledge discovery, Interpretability, Data mining}
}

@inproceedings{ribeiro2016,
	author = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
	title = {“Why Should I Trust You?”: Explaining the Predictions of Any Classifier},
	year = {2016},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	doi = {10.1145/2939672.2939778},
	booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	pages = {1135–1144},
	numpages = {10},
	series = {KDD ’16}
}

@article{staniak2018,
	author = {Mateusz Staniak and Przemys{\l}aw Biecek},
	title = {Explanations of Model Predictions with live and breakDown Packages},
	year = {2018},
	journal = {{The R Journal}},
	doi = {10.32614/RJ-2018-072},
	pages = {395--409},
	volume = {10},
	number = {2}
}

@misc{gosiewska2019,
	author={Alicja Gosiewska and Przemys{\l}aw Biecek},
	title={Do Not Trust Additive Explanations},
	year={2019},
	eprint={1903.11420},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
}

@article{aas2021,
  title = {Explaining individual predictions when features are dependent: More accurate approximations to Shapley values},
  journal = {Artificial Intelligence},
  volume = {298},
  pages = {103502},
  year = {2021},
  issn = {0004-3702},
  doi = {10.1016/j.artint.2021.103502},
  url = {https://www.sciencedirect.com/science/article/pii/S0004370221000539},
  author = {Kjersti Aas and Martin Jullum and Anders Løland},
  keywords = {Feature attribution, Shapley values, Kernel SHAP, Dependence}
}


@InProceedings{covert2021,
  title = 	 { Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression },
  author =       {Covert, Ian and Lee, Su-In},
  booktitle = 	 {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {3457--3465},
  year = 	 {2021},
  editor = 	 {Banerjee, Arindam and Fukumizu, Kenji},
  volume = 	 {130},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {13--15 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v130/covert21a/covert21a.pdf},
  url = 	 {https://proceedings.mlr.press/v130/covert21a.html},
}


@InProceedings{janzing2020,
  title =              {Feature relevance quantification in explainable AI: A causal problem},
  author =       {Janzing, Dominik and Minorics, Lenon and Bloebaum, Patrick},
  booktitle =      {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
  pages =         {2907--2916},
  year =            {2020},
  editor =          {Chiappa, Silvia and Calandra, Roberto},
  volume =        {108},
  series =          {Proceedings of Machine Learning Research},
  month =         {26--28 Aug},
  publisher =    {PMLR},
  pdf =  {http://proceedings.mlr.press/v108/janzing20a/janzing20a.pdf},
  url =    {https://proceedings.mlr.press/v108/janzing20a.html}
}
 
@misc{sundararajan2019,
  doi = {10.48550/ARXIV.1908.08474},
  url = {https://arxiv.org/abs/1908.08474},
  author = {Sundararajan, Mukund and Najmi, Amir},
  title = {The many Shapley values for model explanation},
  publisher = {arXiv},
  year = {2019}
}

@misc{mayer2022b,
  doi = {10.48550/ARXIV.2207.14490},
  url = {https://arxiv.org/abs/2207.14490},
  author = {Mayer, Michael},
  keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {SHAP for additively modeled features in a boosted trees model},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}


@Manual{shapr2021,
    title = {shapr: Prediction Explanation with Dependence-Aware Shapley Values},
    author = {Nikolai Sellereite and Martin Jullum and Annabelle Redelmeier},
    year = {2021},
    note = {R package version 0.2.0},
    url = {https://CRAN.R-project.org/package=shapr},
  }

% data

@article{Noll2018,
	author={Alexander Noll and Robert Salzmann and Mario V. W{\"u}thrich},
	title={Case Study: French Motor Third-Party Liability Claims},
	year={2018},
	doi={10.2139/ssrn.3164764},
	url = {https://ssrn.com/abstract=3164764},
	journal = {{SSRN} Electronic Journal},
}

% ML

@phdthesis{werbos1974,
	added-at = {2008-02-26T11:58:58.000+0100},
	author = {Werbos, P. J.},
	biburl = {https://www.bibsonomy.org/bibtex/2b0644d7aa84be0df0f198d586d341843/schaul},
	citeulike-article-id = {2381655},
	description = {idsia},
	interhash = {4165e2708a0468e89f8305f21ee2c711},
	intrahash = {b0644d7aa84be0df0f198d586d341843},
	keywords = {juergen},
	priority = {2},
	school = {Harvard University},
	timestamp = {2008-02-26T11:59:46.000+0100},
	title = {Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences},
	year = 1974
}

@book{hastie01statisticallearning,
	author = {Trevor Hastie and Robert Tibshirani and Jerome Friedman},
	title = {The Elements of Statistical Learning},
	address = {New York, NY, USA},
	publisher = {Springer New York Inc.},
	series = {Springer Series in Statistics},
	year = {2001},
	edition = {2},
	url = {https://web.stanford.edu/~hastie/ElemStatLearn/},
}

@book{james2021,
  title={An Introduction to Statistical Learning: with Applications in R},
  author={James, G. and Witten, D. and Hastie, T. and Tibshirani, R.},
  isbn={9781071614181},
  series={Springer Texts in Statistics},
  year={2021},
  publisher={Springer US}
}

@article{friedman2001,
	author = "Friedman, Jerome H.",
	doi = "10.1214/aos/1013203451",
	fjournal = "The Annals of Statistics",
	journal = "Ann. Stat.",
	number = "5",
	pages = "1189--1232",
	publisher = "The Institute of Mathematical Statistics",
	title = "Greedy function approximation: A gradient boosting machine.",
	url = "https://doi.org/10.1214/aos/1013203451",
	volume = "29",
	year = "2001"
}

@article{breiman2001,
	author = {Leo Breiman},
	title = {Random Forests},
	journal = {Machine Learning},
	doi = {10.1023/A:1010933404324},
	number = {1},
	pages = {5-32},
	publisher = {Kluwer Academic Publishers},
	volume = {45},
	year = {2001},
}

@Book{breiman1984,
	Title                    = {Classification and Regression Trees},
	Author                   = {Leo Breiman and Jerome Friedman and Charles J. Stone and R.A. Olshen},
	Publisher                = {Chapman and Hall/CRC},
	Year                     = {1984}
}

@article{schapire1990,
	author = {Schapire, Robert E.},
	doi = {10.1023/A:1022648800760},
	issn = {0885-6125},
	journal = {Machine Learning},
	keywords = {Boosting, Learning Machine},
	number = 2,
	pages = {197--227},
	timestamp = {2017-01-09T14:01:11.000+0100},
	title = {The strength of weak learnability},
	url = {http://www.cs.princeton.edu/~schapire/papers/strengthofweak.pdf},
	urldate = {2012-09-24},
	volume = 5,
	year = 1990
}

@article{wuethrich2020,
	title={Bias regularization in neural network models for general insurance pricing},
	author={M. W{\"u}thrich},
	journal={Eur. Actuar. J.},
	year={2020},
	volume={10},
	pages={179--202},
	doi = {10.1007/s13385-019-00215-z}
}

% Additive models

@article{nelder1972,
	URL = {http://www.jstor.org/stable/2344614},
	author = {J. A. Nelder and R. W. M. Wedderburn},
	journal = {J. R. Stat. Soc. Series A (General)},
	number = {3},
	pages = {370--384},
	publisher = {[Royal Statistical Society, Wiley]},
	title = {Generalized Linear Models},
	volume = {135},
	year = {1972},
	doi = {10.2307/2344614}
}

@article{friedman1981,
	URL = {http://www.jstor.org/stable/2287576},
	author = {Jerome H. Friedman and Werner Stuetzle},
	journal = {Journal of the American Statistical Association},
	number = {376},
	pages = {817--823},
	publisher = {[American Statistical Association, Taylor & Francis, Ltd.]},
	title = {Projection Pursuit Regression},
	volume = {76},
	year = {1981},
	doi = {10.2307/2287576}
}

@article{hastie1986,
	author = {Trevor Hastie and Robert Tibshirani},
	title = {{Generalized Additive Models}},
	volume = {1},
	journal = {Stat. Sci.},
	number = {3},
	publisher = {Institute of Mathematical Statistics},
	pages = {297--310},
	year = {1986},
	URL = {https://doi.org/10.1214/ss/1177013604},
	doi = {10.1214/ss/1177013604}
}

@book{hastie1990,
	author = {Hastie, Trevor and Tibshirani, Robert},
	publisher = {Wiley Online Library},
	title = {Generalized additive models},
	year = 1990
}

@article{buehlmann2007,
	author = {Peter Bühlmann and Torsten Hothorn},
	title = {{Boosting Algorithms: Regularization, Prediction and Model Fitting}},
	volume = {22},
	journal = {Stat. Sci.},
	number = {4},
	publisher = {Institute of Mathematical Statistics},
	pages = {477--505},
	year = {2007},
	URL = {https://doi.org/10.1214/07-STS242},
	doi = {10.1214/07-STS242}
}

@misc{hofner2012,
	volume = {120},
	author = {Benjamin Hofner and Andreas Mayr and Nikolay Robinzonov and Matthias Schmid},
	keyword = {boosting, component-wise functional gradient descent, generalized additive models, tutorial},
	title = {Model-based Boosting in R: A Hands-on Tutorial Using the R Package mboost},
	series = {tech},
	year = {2012},
	doi = {10.5282/ubm/epub.12754}
}

@inproceedings{lou2012,
	author = {Lou, Yin and Caruana, Rich and Gehrke, Johannes},
	title = {Intelligible Models for Classification and Regression},
	year = {2012},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2339530.2339556},
	booktitle = {Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	pages = {150--158},
	series = {KDD '12},
	doi = {10.1145/2339530.2339556}
}

@inproceedings{lee2015,
	title={Delta Boosting Machine and its Application in Actuarial Modeling},
	author={Simon C. K. Lee and Sheldon Lin and Katrien Antonio},
	year={2015},
	publisher={Institute of Actuaries of Australia}
}

@book{wood2017,
	title = "Generalized Additive Models: An Introduction with R",
	author = "Wood, {Simon N}",
	year = "2017",
	language = "English",
	publisher = "CRC Press",
	address = "United States",
	edition = "2",
	doi = "10.1201/9781315370279"
}

@article{nori2019,
	author    = {Harsha Nori and
	Samuel Jenkins and
	Paul Koch and
	Rich Caruana},
	title     = {InterpretML: {A} Unified Framework for Machine Learning Interpretability},
	journal   = {CoRR},
	volume    = {abs/1909.09223},
	year      = {2019},
	url       = {http://arxiv.org/abs/1909.09223},
	archivePrefix = {arXiv},
	eprint    = {1909.09223},
	doi = {10.48550/arXiv.1909.00922}
}

@article{agarwal2020,
	author    = {Rishabh Agarwal and
	Nicholas Frosst and
	Xuezhou Zhang and
	Rich Caruana and
	Geoffrey E. Hinton},
	title     = {Neural Additive Models: Interpretable Machine Learning with Neural
	Nets},
	journal   = {CoRR},
	volume    = {abs/2004.13912},
	year      = {2020},
	url       = {https://arxiv.org/abs/2004.13912},
	archivePrefix = {arXiv},
	eprint    = {2004.13912},
	doi = {10.48550/arXiv.2004.13912}
}

@book{koenker2005,
  author = {Koenker, Roger},
  keywords = {Regression Regression:quantile},
  publisher = {Cambridge University Press},
  series = {Econometric Society Monographs},
  title = {Quantile Regression},
  year = {2005},
  doi = {10.1017/CBO9780511754098}
}

@TechReport{umlauf2012,
	author={Nikolaus Umlauf and Daniel Adler and Thomas Kneib and Stefan Lang and Achim Zeileis},
	title={{Structured Additive Regression Models: An R Interface to BayesX}},
	year=2012,
	institution={Faculty of Economics and Statistics, University of Innsbruck},
	type={Working Papers},
	url={https://ideas.repec.org/p/inn/wpaper/2012-10.html},
	number={2012-10}
}

@article{hothorn2010,
	author  = {Torsten Hothorn and Peter B{{\"u}}hlmann and Thomas Kneib and Matthias Schmid and Benjamin Hofner},
	title   = {Model-based Boosting 2.0},
	journal = {J. Mach. Learn. Res.},
	year    = {2010},
	volume  = {11},
	number  = {71},
	pages   = {2109--2113},
	url     = {http://jmlr.org/papers/v11/hothorn10a.html}
}

@misc{ruegamer2021,
	title={Semi-Structured Deep Distributional Regression: Combining Structured Additive Models and Deep Learning}, 
	author={David Rügamer and Chris Kolb and Nadja Klein},
	year={2021},
	eprint={2002.05777},
	archivePrefix={arXiv},
	doi = {10.48550/arXiv.2002.05777}
}

@book{fahrmeir2013,
	author = {Ludwig Fahrmeir and Thomas Kneib and Stefan Lang and Brian Marx},
	title = {Regression: Models, Methods and Applications},
	address = {Berlin},
	publisher = {Springer-Verlag},
	year = {2013},
	doi = {10.1007/978-3-642-34333-9}
}


% Inference

@article{efron1979,
	author = {Bradley Efron},
	title = {{Bootstrap Methods: Another Look at the Jackknife}},
	volume = {7},
	journal = {The Annals of Statistics},
	number = {1},
	publisher = {Institute of Mathematical Statistics},
	pages = {1 -- 26},
	year = {1979},
	doi = {10.1214/aos/1176344552},
	URL = {https://doi.org/10.1214/aos/1176344552}
}

@Book{efron1993,
  Title                    = {An Introduction to the Bootstrap},
  Author                   = {Bradley Efron and Robert J. Tibshirani},
  Publisher                = {Chapman \& Hall/CRC},
  Year                     = {1993},
  Address                  = {Boca Raton, Florida, USA},
  Number                   = {57},
  Series                   = {Monographs on Statistics and Applied Probability}
}

@book{hahn2014,
  author = {Hahn, Gerald J. and Meeker, William Q. and Escobar, Luis A.},
  title = {Statistical Intervals: A Guide for Practitioners},
  year = {2014},
  isbn = {0471687170},
  publisher = {Wiley Publishing},
  edition = {2nd}
}

@article{hothorn2008,
 title={Implementing a Class of Permutation Tests: The coin Package},
 volume={28},
 url={https://www.jstatsoft.org/index.php/jss/article/view/v028i08},
 doi={10.18637/jss.v028.i08},
 number={8},
 journal={Journal of Statistical Software},
 author={Hothorn, Torsten and Hornik, Kurt and van de Wiel, Mark A. and Zeileis, Achim},
 year={2008},
 pages={1–23}
}

@book{fisher1935,
  address = {Edinburgh},
  author = {Fisher, R. A.},
  publisher = {Oliver and Boyd},
  title = {{The Design of Experiments}},
  year = 1935
}


% software

@misc{ic2020,
	author={Michael Mayer},
	title={{Github issue \url{https://github.com/microsoft/LightGBM/issues/2884}}},
	url={\url{https://github.com/microsoft/LightGBM/issues/2884}},
	note={accessed 2022-03-06},
	year={2020}
}

@inproceedings{abadi2016,
	title	= {TensorFlow: A system for large-scale machine learning},
	author	= {Martin Abadi and Paul Barham and Jianmin Chen and Zhifeng Chen and Andy Davis and Jeffrey Dean and Matthieu Devin and Sanjay Ghemawat and Geoffrey Irving and Michael Isard and Manjunath Kudlur and Josh Levenberg and Rajat Monga and Sherry Moore and Derek G. Murray and Benoit Steiner and Paul Tucker and Vijay Vasudevan and Pete Warden and Martin Wicke and Yuan Yu and Xiaoqiang Zheng},
	year	= {2016},
	URL	= {https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf},
	booktitle	= {12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)},
	pages	= {265--283},
	doi = {10.48550/arXiv.1605.08695}
}

@Manual{rlang2021,
	title = {R: A Language and Environment for Statistical Computing},
	author = {{R Core Team}},
	organization = {R Foundation for Statistical Computing},
	address = {Vienna, Austria},
	year = {2021},
	url = {https://www.R-project.org/},
}

@Manual{xgboost2021,
	title = {xgboost: Extreme Gradient Boosting},
	author = {Tianqi Chen and Tong He and Michael Benesty and Vadim Khotilovich and Yuan Tang and Hyunsu Cho and Kailong Chen and Rory Mitchell and Ignacio Cano and Tianyi Zhou and Mu Li and Junyuan Xie and Min Lin and Yifeng Geng and Yutian Li},
	year = {2021},
	note = {R package version 1.4.1.1},
	url = {https://CRAN.R-project.org/package=xgboost},
}

@inproceedings{chen2016,
	author = {Chen, Tianqi and Guestrin, Carlos},
	title = {XGBoost: A Scalable Tree Boosting System},
	year = {2016},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2939672.2939785},
	booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	pages = {785--794},
	series = {KDD '16},
	doi = {10.1145/2939672.2939785}
}

@Manual{lightgbm2021,
  title = {lightgbm: Light Gradient Boosting Machine},
  author = {Guolin Ke and Damien Soukhavong and James Lamb and Qi Meng and Thomas Finley and Taifeng Wang and Wei Chen and Weidong Ma and Qiwei Ye and Tie-Yan Liu and Nikita Titov},
  year = {2021},
  note = {R package version 3.2.1.99},
  url = {https://github.com/Microsoft/LightGBM},
}

@inproceedings{ke2017,
	author = {Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
	booktitle = {Advances in Neural Information Processing Systems},
	pages = {3149--3157},
	publisher = {Curran Associates, Inc.},
	title = {LightGBM: A Highly Efficient Gradient Boosting Decision Tree},
	volume = {30},
	year = {2017}
}

@inproceedings{prokhorenkova2018,
	author = {Prokhorenkova, Liudmila and Gusev, Gleb and Vorobev, Aleksandr and Dorogush, Anna Veronika and Gulin, Andrey},
	title = {CatBoost: Unbiased Boosting with Categorical Features},
	year = {2018},
	publisher = {Curran Associates Inc.},
	address = {Red Hook, NY, USA},
	booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems},
	pages = {6639--6649},
	numpages = {11},
	series = {NIPS'18}
}

@article{wright2017,
	author = {Marvin N. Wright and Andreas Ziegler},
	title = {{ranger}: A Fast Implementation of Random Forests for High Dimensional Data in {C++} and {R}},
	journal = {Journal of Statistical Software},
	year = {2017},
	volume = {77},
	number = {1},
	pages = {1--17},
	doi = {10.18637/jss.v077.i01},
}

@Manual{keras2021,
	title = {keras: R Interface to 'Keras'},
	author = {JJ Allaire and François Chollet},
	year = {2021},
	note = {R package version 2.4.0},
	url = {https://CRAN.R-project.org/package=keras},
}

@book{chollet2018,
  author = {Chollet, François},
  address = {Shelter Island, New York},
  isbn = {1-63835-163-5},
  keywords = {R (Computer program language)},
  language = {eng},
  publisher = {Manning Publications},
  title = {Deep learning with {R}},
  year = {2018},
}

@Manual{flashlight2021,
	title = {flashlight: Shed Light on Black Box Machine Learning Models},
	author = {Michael Mayer},
	year = {2021},
	note = {R package version 0.8.0},
	url = {https://github.com/mayer79/flashlight},
}

@Manual{shapviz2022,
  title = {shapviz: SHAP Visualizations},
  author = {Michael Mayer},
  year = {2022},
  note = {R package version 0.1.0},
  url = {https://CRAN.R-project.org/package=shapviz},
}

@manual{mw2020,
	author = {Michael Mayer},
	title = {MetricsWeighted: Weighted Metrics, Scoring Functions and Performance Measures for Machine Learning},
	year = {2020},
	note = {R package version 0.5.0},
	url = {https://CRAN.R-project.org/package=MetricsWeighted},
  }

@article{biecek2018,
	author = {Przemys{\l}aw Biecek},
	title = {DALEX: Explainers for Complex Predictive Models in R},
	journal = {Journal of Machine Learning Research},
	year = {2018},
	volume = {19},
	pages = {1-5},
	number = {84},
	url = {http://jmlr.org/papers/v19/18-416.html},
}

@article{molnar2018,
	author = {Christoph Molnar and Bernd Bischl and Giuseppe Casalicchio},
	title = {iml: An R package for Interpretable Machine Learning},
	doi = {10.21105/joss.00786},
	url = {http://joss.theoj.org/papers/10.21105/joss.00786},
	year = {2018},
	publisher = {Journal of Open Source Software},
	volume = {3},
	number = {26},
	pages = {786},
	journal = {JOSS},
}

@Manual{mboost2021,
	title = {{mboost}: Model-Based Boosting},
	author = {Torsten Hothorn and Peter Bühlmann and Thomas Kneib and Matthias Schmid and Benjamin Hofner},
	year = {2021},
	note = {{R} package version 2.9-5},
	url = {https://CRAN.R-project.org/package=mboost},
}

@Manual{quantreg2021,
  title = {quantreg: Quantile Regression},
  author = {Roger Koenker},
  year = {2021},
  note = {R package version 5.86},
  url = {https://CRAN.R-project.org/package=quantreg},
}

% insurance


@misc{wuethrich2021,
    title={Data Analytics for Non-Life Insurance Pricing},
    author={Mario V. Wüthrich and Christoph Buser},
    year={2021},
    doi = {10.2139/ssrn.2870308}
}

@book{ohlsson2015,
  title={Non-Life Insurance Pricing with Generalized Linear Models},
  author={Ohlsson, E. and Johansson, B.},
  isbn={9783642107900},
  lccn={2010923536},
  series={EAA Series},
  year={2015},
  publisher={Springer Berlin Heidelberg}
}

@book{wilkinson05,
  author = {Wilkinson, Leland},
  title = {The Grammar of Graphics (Statistics and Computing)},
  year = {2005},
  isbn = {0387245448},
  publisher = {Springer-Verlag},
  address = {Berlin, Heidelberg}
}

@book{wickham2017,
  title = {R for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
  author = {Hadley Wickham and Garrett Grolemund},
  publisher = {O'Reilly Media},
  url = {http://r4ds.had.co.nz/},
  year = 2017
}

@Book{xie2018,
    title = {R Markdown: The Definitive Guide},
    author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
    publisher = {Chapman and Hall/CRC},
    address = {Boca Raton, Florida},
    year = {2018},
    note = {ISBN 9781138359338},
    url = {https://bookdown.org/yihui/rmarkdown},
  }

@book{wickham2015,
  title = {Advanced {R}},
  author = {Hadley Wickham},
  publisher = {CRC Press},
  address = {Boca Raton FL},
  year = {2015},
  url = {https://adv-r.hadley.nz/index.html}
}

@book{wickham2015r,
  title={R Packages: Organize, Test, Document, and Share Your Code},
  author={Hadley Wickham},
  year={2015},
  publisher={O'Reilly Media},
  url = {https://r-pkgs.org/}
}