@article{54911,
  author       = {{Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  issn         = {{0888-613X}},
  journal      = {{International Journal of Approximate Reasoning}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning decision catalogues for situated decision making: The case of scoring systems}}},
  doi          = {{10.1016/j.ijar.2024.109190}},
  volume       = {{171}},
  year         = {{2024}},
}

@article{54910,
  author       = {{Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  issn         = {{0888-613X}},
  journal      = {{International Journal of Approximate Reasoning}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning decision catalogues for situated decision making: The case of scoring systems}}},
  doi          = {{10.1016/j.ijar.2024.109190}},
  volume       = {{171}},
  year         = {{2024}},
}

@article{54907,
  author       = {{Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  issn         = {{0888-613X}},
  journal      = {{International Journal of Approximate Reasoning}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning decision catalogues for situated decision making: The case of scoring systems}}},
  doi          = {{10.1016/j.ijar.2024.109190}},
  volume       = {{171}},
  year         = {{2024}},
}

@inproceedings{57645,
  author       = {{Heid, Stefan and Kornowicz, Jaroslaw and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Thommes, Kirsten}},
  booktitle    = {{PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE}},
  pages        = {{233}},
  title        = {{{Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems}}},
  volume       = {{21}},
  year         = {{2024}},
}

@inproceedings{51373,
  author       = {{Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  booktitle    = {{26th International Conference on Discovery Science }},
  isbn         = {{9783031452741}},
  issn         = {{0302-9743}},
  location     = {{Porto}},
  pages        = {{189--203}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Probabilistic Scoring Lists for Interpretable Machine Learning}}},
  doi          = {{10.1007/978-3-031-45275-8_13}},
  volume       = {{14050}},
  year         = {{2023}},
}

@inbook{54613,
  author       = {{Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}},
  booktitle    = {{On-The-Fly Computing – Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  keywords     = {{dice ngonga sfb901 sherif}},
  pages        = {{85–104}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Configuration and Evaluation}}},
  doi          = {{10.5281/zenodo.8068466}},
  volume       = {{412}},
  year         = {{2023}},
}

@inbook{54909,
  author       = {{Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  booktitle    = {{Discovery Science}},
  isbn         = {{9783031452741}},
  issn         = {{0302-9743}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Probabilistic Scoring Lists for Interpretable Machine Learning}}},
  doi          = {{10.1007/978-3-031-45275-8_13}},
  year         = {{2023}},
}

@inbook{45884,
  author       = {{Hanselle, Jonas Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}},
  booktitle    = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}},
  editor       = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}},
  pages        = {{85--104}},
  publisher    = {{Heinz Nixdorf Institut, Universität Paderborn}},
  title        = {{{Configuration and Evaluation}}},
  doi          = {{10.5281/zenodo.8068466}},
  volume       = {{412}},
  year         = {{2023}},
}

@inproceedings{51209,
  author       = {{Hanselle, Jonas Manuel and Kornowicz, Jaroslaw and Heid, Stefan and Thommes, Kirsten and Hüllermeier, Eyke}},
  booktitle    = {{LWDA’23: Learning, Knowledge, Data, Analysis. }},
  editor       = {{Leyer, M and Wichmann, J}},
  issn         = {{1613-0073}},
  title        = {{{Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain}}},
  year         = {{2023}},
}

@inproceedings{34103,
  abstract     = {{It is well known that different algorithms perform differently well on an
instance of an algorithmic problem, motivating algorithm selection (AS): Given
an instance of an algorithmic problem, which is the most suitable algorithm to
solve it? As such, the AS problem has received considerable attention resulting
in various approaches - many of which either solve a regression or ranking
problem under the hood. Although both of these formulations yield very natural
ways to tackle AS, they have considerable weaknesses. On the one hand,
correctly predicting the performance of an algorithm on an instance is a
sufficient, but not a necessary condition to produce a correct ranking over
algorithms and in particular ranking the best algorithm first. On the other
hand, classical ranking approaches often do not account for concrete
performance values available in the training data, but only leverage rankings
composed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon
foreSts - a new algorithm selector leveraging special forests, combining the
strengths of both approaches while alleviating their weaknesses. HARRIS'
decisions are based on a forest model, whose trees are created based on splits
optimized on a hybrid ranking and regression loss function. As our preliminary
experimental study on ASLib shows, HARRIS improves over standard algorithm
selection approaches on some scenarios showing that combining ranking and
regression in trees is indeed promising for AS.}},
  author       = {{Fehring, Lukass and Hanselle, Jonas Manuel and Tornede, Alexander}},
  booktitle    = {{Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022}},
  location     = {{Baltimore}},
  title        = {{{HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection}}},
  year         = {{2022}},
}

@inproceedings{23779,
  abstract     = {{Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. 
Im Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden.}},
  author       = {{Bernijazov, Ruslan and Dicks, Alexander and Dumitrescu, Roman and Foullois, Marc and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Karakaya, Gökce and Ködding, Patrick and Lohweg, Volker and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Panzner, Melina and Soltenborn, Christian}},
  booktitle    = {{Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)}},
  keywords     = {{Artificial Intelligence Product Creation Literature Review}},
  location     = {{Montreal, Kanada}},
  title        = {{{A Meta-Review on Artificial Intelligence in Product Creation}}},
  year         = {{2021}},
}

@unpublished{30866,
  abstract     = {{Automated machine learning (AutoML) strives for the automatic configuration
of machine learning algorithms and their composition into an overall (software)
solution - a machine learning pipeline - tailored to the learning task
(dataset) at hand. Over the last decade, AutoML has developed into an
independent research field with hundreds of contributions. While AutoML offers
many prospects, it is also known to be quite resource-intensive, which is one
of its major points of criticism. The primary cause for a high resource
consumption is that many approaches rely on the (costly) evaluation of many
machine learning pipelines while searching for good candidates. This problem is
amplified in the context of research on AutoML methods, due to large scale
experiments conducted with many datasets and approaches, each of them being run
with several repetitions to rule out random effects. In the spirit of recent
work on Green AI, this paper is written in an attempt to raise the awareness of
AutoML researchers for the problem and to elaborate on possible remedies. To
this end, we identify four categories of actions the community may take towards
more sustainable research on AutoML, i.e. Green AutoML: design of AutoML
systems, benchmarking, transparency and research incentives.}},
  author       = {{Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}},
  booktitle    = {{arXiv:2111.05850}},
  title        = {{{Towards Green Automated Machine Learning: Status Quo and Future Directions}}},
  year         = {{2021}},
}

@inproceedings{21198,
  author       = {{Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  location     = {{Delhi, India}},
  title        = {{{Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data}}},
  year         = {{2021}},
}

@inproceedings{17408,
  author       = {{Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  booktitle    = {{KI 2020: Advances in Artificial Intelligence}},
  title        = {{{Hybrid Ranking and Regression for Algorithm Selection}}},
  year         = {{2020}},
}