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  author    = {Marco Tulio Ribeiro and
               Sameer Singh and
               Carlos Guestrin},
  title     = {"Why Should {I} Trust You?": Explaining the Predictions of Any Classifier},
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               13-17, 2016},
  pages     = {1135--1144},
  year      = {2016},
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}

@article{DALEX,
	author = {{Biecek}, Przemysław},
    title = "{DALEX: explainers for complex predictive models}",
    url = {http://arxiv.org/abs/1806.08915},
	eprint = {1806.08915},
	primaryClass = "stat.ML",
	keywords = {Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Applications},
    year = 2018,
	adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180608915B},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@incollection{NIPS2017_7062,
title = {A Unified Approach to Interpreting Model Predictions},
author = {Lundberg, Scott M and Lee, Su-In},
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year = {2017},
publisher = {Curran Associates, Inc.},
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}

@book{Gill2017MachineLI,
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  editor={Angela Bartz},
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}

@ARTICLE{2018arXiv180401955S,
  author = {Mateusz Staniak and Przemysław 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},
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}

@article{Truong2019TowardsAM,
  title={Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools},
  author={Anh Truong and Austin Walters and Jeremy Goodsitt and Keegan Hines and C. Bayan Bruss and Reza Farivar},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.05557}
}

@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2019},
    url = {https://www.R-project.org/},
  }

@article{bostock2016d3,
  title={D3.js-data-driven documents (2016)},
  author={Bostock, Mike},
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  year={2016}
}

@article{Romaszko2019,
  doi = {10.21105/joss.01444},
  url = {https://doi.org/10.21105/joss.01444},
  year = {2019},
  month = jun,
  publisher = {The Open Journal},
  volume = {4},
  number = {38},
  pages = {1444},
  author = {Kamil Romaszko and Magda Tatarynowicz and Mateusz Urba{\'{n}}ski and Przemys{\l}aw Biecek},
  title = {{modelDown}: automated website generator with interpretable documentation for predictive machine learning models},
  journal = {Journal of Open Source Software}
}

@article{gosiewska2019ibreakdown,
  title={iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models},
  author={Gosiewska, Alicja and Biecek, Przemyslaw},
  journal={arXiv preprint arXiv:1903.11420},
  year={2019}
}

@Manual{iBreakDown,
  title = {iBreakDown: Model Agnostic Instance Level Variable Attributions},
  author = {Przemyslaw Biecek and Alicja Gosiewska and Hubert Baniecki and Adam Izdebski},
  year = {2019},
  note = {R package version 0.9.9},
  url = {https://CRAN.R-project.org/package=iBreakDown},
}

@Manual{ingredients,
    title = {ingredients: Effects and Importances of Model Ingredients},
    author = {Przemyslaw Biecek and Hubert Baniecki and Adam Izdebski and Katarzyna Pekala},
    year = {2019},
    note = {https://ModelOriented.github.io/ingredients/,
https://github.com/ModelOriented/ingredients},
    url = {http://CRAN.R-project.org/package=ingredients}
}

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

@Manual{tfexplain,
    title = {Interpretability Methods for tf.keras models with Tensorflow 2.0},
    author = {Raphael Meudec},
    year = {2019},
    note = {https://tf-explain.readthedocs.io},
    url = {https://tf-explain.readthedocs.io}
}

@Manual{sklearnexplain,
    title = {sklearn explain},
    author = {Antoine Carme},
    year = {2019},
    note = {https://github.com/antoinecarme/sklearn_explain},
    url = {https://github.com/antoinecarme/sklearn_explain}
}

@Manual{InterpretML,
    title = {InterpretML},
    author = {Samuel Jenkins and Harsha Nori and Paul Koch and Rich Caruana},
    year = {2019},
    note = {https://github.com/microsoft/interpret},
    url = {https://github.com/microsoft/interpret}
}

@ARTICLE{2019arXiv190402101S,
       author = {{Staniak}, Mateusz and {Biecek}, Przemyslaw},
        title = "{The Landscape of R Packages for Automated Exploratory Data Analysis}",
      journal = {arXiv e-prints},
     keywords = {Statistics - Computation, Computer Science - Machine Learning, Statistics - Machine Learning},
         year = "2019",
        month = "Mar",
          eid = {arXiv:1904.02101},
        pages = {arXiv:1904.02101},
archivePrefix = {arXiv},
       eprint = {1904.02101},
 primaryClass = {stat.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190402101S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@Manual{ceterisParibus,
    title = {ceterisParibus: Ceteris Paribus Profiles},
    author = {Przemyslaw Biecek},
    year = {2019},
    note = {R package version 0.3.1},
    url = {https://CRAN.R-project.org/package=ceterisParibus}
}

@article{fisher2018all,
  title={All models are wrong but many are useful: Variable importance for black-box, proprietary, or misspecified prediction models, using model class reliance},
  author={Fisher, Aaron and Rudin, Cynthia and Dominici, Francesca},
  journal={arXiv preprint arXiv:1801.01489},
  year={2018}
}

@article{RJ-2017-016,
  author = {Greenwell, Brandon M.},
  title = {{pdp: An R Package for Constructing Partial Dependence Plots}},
  year = {2017},
  journal = {{The R Journal}},
  url = {http://journal.r-project.org/archive/2017/RJ-2017-016/index.html},
  pages = {421--436},
  volume = {9},
  number = {1},
  doi = {10.32614/rj-2017-016}
}

@article{apley2016visualizing,
  title={Visualizing the effects of predictor variables in black box supervised learning models},
  author={Apley, Daniel W},
  journal={arXiv preprint arXiv:1612.08468},
  year={2016}
}

@Manual{pmeed,
    title = {Predictive Models: Explore, Explain, and Debug},
    author = {Przemyslaw Biecek and Tomasz Burzykowski},
    year = {2019},
    note = {https://pbiecek.github.io/PM_VEE/},
    url = {https://pbiecek.github.io/PM_VEE/}
}

