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@article{NeuroBayes,
title={The NeuroBayes neural network package},
author={M. Feindt and U. Kerzel},
journal={NIM A},
volume={559},
number={1},
pages={190--194},
year={2006},
url={http://www.sciencedirect.com/science/article/pii/S0168900205022679},
}
@inproceedings{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, San Francisco, CA, USA, August 13-17, 2016},
pages = {1135--1144},
year = {2016},
}
@article{SHAP,
author = {Scott Lundberg and Su{-}In Lee},
title = {A unified approach to interpreting model predictions},
journal = {CoRR},
volume = {abs/1705.07874},
year = {2017},
url = {http://arxiv.org/abs/1705.07874},
archivePrefix = {arXiv},
eprint = {1705.07874},
timestamp = {Wed, 07 Jun 2017 14:42:10 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/LundbergL17},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{Shapeley1953,
author = {L. S. Shapley},
title = {A Value for n-Person Games},
journal = {Cntributions to the Theory of Games II, Annals of Mathematics Studies},
volume = {28},
year = {1953},
pages = {307--317}
}
@article{Ehrenberg1959,
ISSN = {00359254, 14679876},
URL = {http://www.jstor.org/stable/2985810},
author = {A. S. C. Ehrenberg},
journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)},
number = {1},
pages = {26--41},
publisher = {[Wiley, Royal Statistical Society]},
title = {The Pattern of Consumer Purchases},
volume = {8},
year = {1959}
}
@book{GAM,
author = {T.J. Hastie and R.J. Tibshirani},
title = {Generalized Additive Models},
publisher = {Chapman and Hall/CRC},
year = 1990,
isbn = {978-0-412-34390-2}
}
@misc{kaggle_data,
howpublished = {\url{https://www.kaggle.com/c/demand-forecasting-kernels-only/data}}
}
@article{Wright2015,
author = {Wright, Stephen J.},
title = {Coordinate descent algorithms},
journal = {Mathematical Programming},
year = {2015},
month = {Jun},
day = {01},
volume = {151},
number = {1},
pages = {3--34},
issn = {1436-4646},
doi = {10.1007/s10107-015-0892-3},
url = {https://doi.org/10.1007/s10107-015-0892-3}
}
@book{molnar2019,
title = {Interpretable Machine Learning},
author = {Christoph Molnar},
note = {\url{https://christophm.github.io/interpretable-ml-book/}},
year = {2019},
subtitle = {A Guide for Making Black Box Models Explainable}
}
@misc{GoogleWhatIf,
title = {The What-If Tool: Code-Free Probing of Machine Learning Models},
author = {Google},
howpublished = {\url{https://github.com/tensorflow/tensorboard/tree/master/tensorboard/plugins/interactive_inference}}
}
@misc{eli5,
author = {TeamHG-Memex},
title = {eli5: A library for debugging/inspecting machine learning classifiers and explaining their predictions},
howpublished = {\url{https://github.com/TeamHG-Memex/eli5}}
}
@article{Breiman2001,
author = {Breiman, Leo},
title = {Random Forests},
journal = {Machine Learning},
year = {2001},
month = {Oct},
day = {01},
volume = {45},
number = {1},
pages = {5--32},
issn = {1573-0565},
doi = {10.1023/A:1010933404324},
url = {https://doi.org/10.1023/A:1010933404324}
}
@article{friedman2001,
author = {Friedman, Jerome H.},
doi = {10.1214/aos/1013203451},
fjournal = {The Annals of Statistics},
journal = {Ann. Statist.},
month = {10},
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{Simonyan2013,
author = {Karen Simonyan and Andrea Vedaldi and Andrew Zisserman},
title = {Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps},
year = {2013},
journal = {arXiv preprint arXiv:1312.6034}
}
@article{Zeiler2013,
author = {Matthew D Zeiler and Rob Fergus},
title = {Visualizing and Understanding Convolutional Networks},
year = {2013},
journal = {arXiv preprint arXiv:1311.2901}
}
@article{GLM,
ISSN = {00359238},
URL = {http://www.jstor.org/stable/2344614},
author = {J. A. Nelder and R. W. M. Wedderburn},
journal = {Journal of the Royal Statistical Society. Series A (General)},
number = {3},
pages = {370--384},
publisher = {[Royal Statistical Society, Wiley]},
title = {Generalized Linear Models},
volume = {135},
year = {1972}
}
@misc{Interprete,
author = {Microsoft},
title = {InterpretML},
howpublished = {\url{https://github.com/microsoft/interpret}}
}
@inproceedings{GAM_Microsoft,
author = {Caruana, Rich and Lou, Yin and Gehrke, Johannes and Koch, Paul and Sturm, Marc and Elhadad, Noemie},
title = {Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission},
booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
series = {KDD '15},
year = {2015},
isbn = {978-1-4503-3664-2},
location = {Sydney, NSW, Australia},
pages = {1721--1730},
numpages = {10},
url = {http://doi.acm.org/10.1145/2783258.2788613},
doi = {10.1145/2783258.2788613},
acmid = {2788613},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {additive models, classification, healthcare, intelligibility, interaction detection, logistic regression, risk prediction},
}
@inproceedings{Boser1992,
doi = {10.1145/130385.130401},
url = {https://doi.org/10.1145/130385.130401},
year = {1992},
publisher = {{ACM} Press},
author = {Bernhard E. Boser and Isabelle M. Guyon and Vladimir N. Vapnik},
title = {A training algorithm for optimal margin classifiers},
booktitle = {Proceedings of the fifth annual workshop on Computational learning theory - {COLT} {\textquotesingle}92}
}
@book{PearlCausality,
author={Judea Pearl},
title={Causality: Models, Reasoning and Inference},
publisher={Cambridge University Press},
edition={2},
year={2009},
isbn={978-0521895606}
}
@article{PearlML,
author = {Pearl, Judea},
title = {The Seven Tools of Causal Inference, with Reflections on Machine Learning},
year = {2019},
issue_date = {March 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {62},
number = {3},
issn = {0001-0782},
url = {https://doi.org/10.1145/3241036},
doi = {10.1145/3241036},
abstract = {The kind of causal inference seen in natural human thought can be "algorithmitized" to help produce human-level machine intelligence.},
journal = {Commun. ACM},
month = {Feb},
pages = {54--60},
numpages = {7}
}
@article{Rubin,
author = {Donald B. Rubin},
title = {Causal Inference Using Potential Outcomes},
journal = {Journal of the American Statistical Association},
volume = {100},
number = {469},
pages = {322--331},
year = {2005},
publisher = {Taylor & Francis},
doi = {10.1198/016214504000001880},
URL = {https://doi.org/10.1198/016214504000001880},
eprint = {https://doi.org/10.1198/016214504000001880}
}
@article{RCT,
title = {A method for assessing the quality of a randomized control trial},
journal = {Controlled Clinical Trials},
volume = {2},
number = {1},
pages = {31--49},
year = {1981},
issn = {0197-2456},
doi = {https://doi.org/10.1016/0197-2456(81)90056-8},
url = {http://www.sciencedirect.com/science/article/pii/0197245681900568},
author = {Thomas C. Chalmers and Harry Smith and Bradley Blackburn and Bernard Silverman and Biruta Schroeder and Dinah Reitman and Alexander Ambroz}
}
@article{propensity,
author = {Rosenbaum, Paul R. and Rubin, Donald B.},
title = "{The central role of the propensity score in observational studies for causal effects}",
journal = {Biometrika},
volume = {70},
number = {1},
pages = {41--55},
year = {1983},
month = {04},
issn = {0006-3444},
doi = {10.1093/biomet/70.1.41},
url = {https://doi.org/10.1093/biomet/70.1.41},
eprint = {https://academic.oup.com/biomet/article-pdf/70/1/41/662954/70-1-41.pdf},
}
@article{PhysRevD.84.012003,
title = {Measurements of the properties of ${\ensuremath{\Lambda}}_{c}(2595)$, ${\ensuremath{\Lambda}}_{c}(2625)$, ${\ensuremath{\Sigma}}_{c}(2455)$, and ${\ensuremath{\Sigma}}_{c}(2520)$ baryons},
author = {CDF-Collaboration},
collaboration = {CDF Collaboration},
journal = {Phys. Rev. D},
volume = {84},
issue = {1},
pages = {012003},
numpages = {17},
year = {2011},
month = {Jul},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.84.012003},
url = {https://link.aps.org/doi/10.1103/PhysRevD.84.012003}
}
@article{PhysRevD.86.032007,
title = {Measurement of $CP$-violation asymmetries in ${D}^{0}\ensuremath{\rightarrow}{K}_{S}^{0}{\ensuremath{\pi}}^{\mathbf{+}}{\ensuremath{\pi}}^{\mathbf{\ensuremath{-}}}$},
author = {CDF-Collaboration},
collaboration = {CDF Collaboration},
journal = {Phys. Rev. D},
volume = {86},
issue = {3},
pages = {032007},
numpages = {15},
year = {2012},
month = {Aug},
publisher = {American Physical Society},
doi = {10.1103/PhysRevD.86.032007},
url = {https://link.aps.org/doi/10.1103/PhysRevD.86.032007}
}
@article{Pivk_2005,
title={: A statistical tool to unfold data distributions},
volume={555},
ISSN={0168-9002},
url={http://dx.doi.org/10.1016/j.nima.2005.08.106},
DOI={10.1016/j.nima.2005.08.106},
number={1-2},
journal={Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
publisher={Elsevier BV},
author={Pivk, M. and Le Diberder, F.R.},
year={2005},
month={Dec},
pages={356?369}
}
@article{wick2021demand,
author = {F. Wick and U. Kerzel and M. Hahn and M. Wolf and T. Singhal and D. Stemmer and J. Ernst and M. Feindt},
title = {Demand Forecasting of Individual Probability Density Functions with Machine Learning},
year = {2021},
journal = {arXiv preprint arXiv:2009.07052v2}
}