@book{Buecker2008,
  author    = {B{\"u}cker, Michael},
  title     = {Lokale Diskrimination diskreter Daten},
  address   = {Dortmund},
  year      = {2008},
  language  = {mul}
}
@article{BueckervanKampenKraemer2013,
  author    = {B{\"u}cker, Michael and van Kampen, Maarten and Kr{\"a}mer, Walter},
  title     = {Reject inference in consumer credit scoring with nonignorable missing data},
  series = {Journal of Banking \& Finance},
  volume    = {37},
  journal   = {Journal of Banking \& Finance},
  number    = {3},
  doi       = {10.1016/j.jbankfin.2012.11.002},
  pages     = {1040 -- 1045},
  year      = {2013},
  language  = {en}
}
@article{BueckerKraemerArnold2012,
  author    = {B{\"u}cker, Michael and Kr{\"a}mer, Walter and Arnold, Matthias},
  title     = {A Hausman test for non-ignorability},
  series = {Economics Letters},
  volume    = {114},
  journal   = {Economics Letters},
  number    = {1},
  doi       = {10.1016/j.econlet.2011.08.025},
  pages     = {23 -- 25},
  year      = {2012},
  language  = {en}
}
@article{HoopsBuecker2014,
  author    = {Hoops, Christian and B{\"u}cker, Michael},
  title     = {Determinants, Moderators and Consequences of Organizational Interaction Orientation},
  series = {Journal of Entrepreneurship Management and Innovation},
  volume    = {9},
  journal   = {Journal of Entrepreneurship Management and Innovation},
  number    = {4},
  pages     = {73 -- 100},
  year      = {2014},
  language  = {en}
}
@article{KraemerBuecker2011,
  author    = {Kr{\"a}mer, Walter and B{\"u}cker, Michael},
  title     = {Probleme des Qualit{\"a}tsvergleichs von Kreditausfallprognosen},
  series = {AStA Wirtschafts- und Sozialstatistisches Archiv},
  volume    = {5},
  journal   = {AStA Wirtschafts- und Sozialstatistisches Archiv},
  number    = {1},
  doi       = {10.1007/s11943-011-0096-0},
  pages     = {39 -- 58},
  year      = {2011},
  language  = {de}
}
@incollection{BueckerSzepannekWeihs2010,
  author    = {B{\"u}cker, Michael and Szepannek, Gero and Weihs, Claus},
  title     = {Local Classification of Discrete Variables by Latent Class Models},
  series = {Classification as a Tool for Research, Studies in Classification, Data Analysis, and Knowledge Organization},
  booktitle = {Classification as a Tool for Research, Studies in Classification, Data Analysis, and Knowledge Organization},
  publisher = {Springer},
  isbn      = {978-3-642-76307-6},
  doi       = {10.1007/978-3-642-10745-0_13},
  pages     = {127 -- 135},
  year      = {2010},
  language  = {en}
}
@book{Buecker2011,
  author    = {B{\"u}cker, Michael},
  title     = {Statistische Modelle mit nicht-ignorierbar fehlender Zielgr{\"o}ße und Anwendung in der reject inference},
  address   = {Dortmund},
  doi       = {10.17877/DE290R-555},
  year      = {2011},
  language  = {de}
}
@article{BueckerKraemer2011,
  author    = {B{\"u}cker, Michael and Kr{\"a}mer, Walter},
  title     = {Statistischer Qualit{\"a}tsvergleich von Kreditausfallprognosen},
  series = {Discussion Paper SFB 823},
  volume    = {30},
  journal   = {Discussion Paper SFB 823},
  year      = {2011},
  language  = {de}
}
@article{BueckerKraemer2011,
  author    = {B{\"u}cker, Michael and Kr{\"a}mer, Walter},
  title     = {Reject inference in consumer credit scoring with nonignorable missing data},
  series = {Discussion Paper SFB 823},
  volume    = {1},
  journal   = {Discussion Paper SFB 823},
  year      = {2011},
  language  = {en}
}
@inproceedings{BueckerSzepannekBieceketal.2019,
  author    = {B{\"u}cker, Michael and Szepannek, Gero and Biecek, Przemyslaw and Gosiewska, Alicja and Staniak, Mateusz},
  title     = {Transparency of Machine Learning Models in Credit Scoring},
  series = {CRC Conference XVI Papers},
  booktitle = {CRC Conference XVI Papers},
  editor    = {Crook, Jonathan},
  publisher = {Credit Research Center, University of Edinburgh},
  address   = {Edinburgh},
  pages     = {1 -- 1},
  year      = {2019},
  abstract  = {A major requirement for Credit Scoring models is of course to provide a risk prediction that is as accurate as possible. In addition, regulators demand these models to be transparent and auditable. Thus, in Credit Scoring very simple Predictive Models such as Logistic Regression or Decision Trees are still widely used and the superior predictive power of modern Machine Learning algorithms cannot be fully leveraged. A lot of potential is therefore missed, leading to higher reserves or more credit defaults. This talk presents an overview of techniques that are able to make "black box" machine learning models transparent and demonstrate how they can be applied in Credit Scoring. We use the DALEX set of tools to compare a traditional scoring approach with state of the art Machine Learning models and asses both approaches in terms of interpretability and predictive power. Results show that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.},
  language  = {en}
}
@article{BueckerSzepannekGosiewskaetal.2020,
  author    = {B{\"u}cker, Michael and Szepannek, Gero and Gosiewska, Alicja and Biecek, Przemyslaw},
  title     = {Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring},
  series = {arXiv},
  volume    = {2009.13384},
  journal   = {arXiv},
  pages     = {1 -- 30},
  year      = {2020},
  abstract  = {A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic regression or decision trees are still widely used and the superior predictive power of modern machine learning algorithms cannot be fully leveraged. Significant potential is therefore missed, leading to higher reserves or more credit defaults. This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable. Following this framework, we present an overview of techniques, demonstrate how they can be applied in credit scoring and how results compare to the interpretability of score cards. A real world case study shows that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.},
  language  = {en}
}
@article{BueckerSzepannekGosiewskaetal.2021,
  author    = {B{\"u}cker, Michael and Szepannek, Gero and Gosiewska, Alicja and Biecek, Przemyslaw},
  title     = {Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring},
  series = {Journal of the Operational Research Society},
  journal   = {Journal of the Operational Research Society},
  doi       = {10.1080/01605682.2021.1922098},
  year      = {2021},
  language  = {en}
}
@unpublished{BueckerSchluesener2022,
  author    = {B{\"u}cker, Michael and Schl{\"u}sener, Niels},
  title     = {Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models},
  series = {arXiv},
  journal   = {arXiv},
  publisher = {arXiv.org},
  doi       = {10.48550/arXiv.2212.08363},
  pages     = {1 -- 9},
  year      = {2022},
  language  = {en}
}