@phdthesis{42066,
  author       = {{Kruse, Daniel}},
  isbn         = {{9783947647071}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts}},
  title        = {{{Teilautomatisierte Parameteridentifikation für die Validierung von Dynamikmodellen im modellbasierten Entwurf mechatronischer Systeme}}},
  volume       = {{388}},
  year         = {{2019}},
}

@phdthesis{28366,
  author       = {{Pai, Arathi}},
  isbn         = {{978-3-947647-11-8}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}},
  title        = {{{Computationally Efficient Modelling and Precision Position and Force Control of SMA Actuators}}},
  volume       = {{392}},
  year         = {{2019}},
}

@inproceedings{14898,
  author       = {{Schubert, Philipp and Leer, Richard and Hermann, Ben and Bodden, Eric}},
  booktitle    = {{Proceedings of the 8th ACM SIGPLAN International Workshop on State Of the Art in Program Analysis  - SOAP 2019}},
  isbn         = {{9781450367202}},
  title        = {{{Know your analysis: how instrumentation aids understanding static analysis}}},
  doi          = {{10.1145/3315568.3329965}},
  year         = {{2019}},
}

@misc{28361,
  author       = {{Gausemeier, Jürgen}},
  isbn         = {{978-3-947647-04-0}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}},
  title        = {{{Vorausschau und Technologieplanung. 14. Symposium für Vorausschau und Technologieplanung, Heinz Nixdorf Institut, 8. und 9. November 2018}}},
  volume       = {{385}},
  year         = {{2018}},
}

@phdthesis{26249,
  author       = {{Weber, Jens}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}},
  title        = {{{Modellbasierte Werkstück- und Werkzeugpositionierung zur Reduzierung der Zykluszeit in NC-Programmen}}},
  volume       = {{377}},
  year         = {{2018}},
}

@phdthesis{26250,
  author       = {{Kage, Martin}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}},
  title        = {{{Systematik zur Positionierung in technologieinduzierten Wertschöpfungsnetzwerken}}},
  volume       = {{383}},
  year         = {{2018}},
}

@phdthesis{26251,
  author       = {{Dülme, Christian}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}},
  title        = {{{Systematik zur zukunftsorientierten Konsolidierung variantenreicher Produktprogramme}}},
  volume       = {{384}},
  year         = {{2018}},
}

@phdthesis{26252,
  author       = {{Schneider, Marcel}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}},
  title        = {{{Spezifikationstechnik zur Beschreibung und Analyse von Wertschöpfungssystemen}}},
  volume       = {{386}},
  year         = {{2018}},
}

@phdthesis{26253,
  author       = {{Echterhoff, Benedikt}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}},
  title        = {{{Methodik zur Einführung innovativer Geschäftsmodelle in etablierten Unternehmen}}},
  volume       = {{387}},
  year         = {{2018}},
}

@article{3510,
  abstract     = {{Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.}},
  author       = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  issn         = {{1573-0565}},
  journal      = {{Machine Learning}},
  keywords     = {{AutoML, Hierarchical Planning, HTN planning, ML-Plan}},
  location     = {{Dublin, Ireland}},
  pages        = {{1495--1515}},
  publisher    = {{Springer}},
  title        = {{{ML-Plan: Automated Machine Learning via Hierarchical Planning}}},
  doi          = {{10.1007/s10994-018-5735-z}},
  year         = {{2018}},
}

@unpublished{17713,
  author       = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}},
  publisher    = {{Arxiv}},
  title        = {{{Automated Multi-Label Classification based on ML-Plan}}},
  year         = {{2018}},
}

@unpublished{17714,
  author       = {{Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}},
  title        = {{{Automated machine learning service composition}}},
  year         = {{2018}},
}

@inproceedings{21991,
  author       = {{Graessler, Iris and Scholle, Philipp and Hentze, Julian and Oleff, Christian}},
  booktitle    = {{Proceedings of the DESIGN 2018 15th International Design Conference}},
  isbn         = {{9789537738594}},
  title        = {{{SEMI-AUTOMATIZED ASSESSMENT OF REQUIREMENT INTERRELATIONS}}},
  doi          = {{10.21278/idc.2018.0298}},
  year         = {{2018}},
}

@inproceedings{21998,
  abstract     = {{Changing requirements have a broad impact on product development processes. In this paper, a novel approach towards structuring requirements is proposed. Based on a requirements list, interrelations of requirements are assessed semi-automatically by a rule basis. Here, generic interrelations funded on either physical fundamentals or working principles are recorded. By this approach, requirements structure matrices are derived semi-automatically. Combined with selecting critical requirements based on structured criterions, iterations due to changing requirements will be reduced.}},
  author       = {{Gräßler, I. and Scholle, P. and Hentze, J. and Oleff, C.}},
  booktitle    = {{15th International Design Conference}},
  pages        = {{S. 325--334}},
  title        = {{{Semi-Automatized Assessment of Requirement Interrelations}}},
  doi          = {{10.21278/idc.2018.0298}},
  volume       = {{15}},
  year         = {{2018}},
}

@inproceedings{21999,
  abstract     = {{In this paper, a novel approach towards risk classification of requirements of additively manufactured (AM) products is presented. The classification of an individual requirement is based on two aspects: a) influence factors implying dynamics of its specification and b) network indices quantifying the degree of cross-linkage with other requirements. Both aspects can indicate requirement changes. Influences on a requirement are assessed by a priority index based on criteria ‘uncertainty’, ‘dynamics’ and ‘relevance for product development’. Effects from other requirements are captured by network theoretical indices such as active and passive sum. In the end, requirements are classified accord-ing to their criticality. By this approach, requirements can be identified which might strongly affect the product development process of AM products.}},
  author       = {{Gräßler, I. and Oleff, C. and Scholle, P.}},
  booktitle    = {{Design for X - Beiträge zum 29. DfX-Symposium}},
  pages        = {{333--344}},
  title        = {{{Methode zur Bewertung von Anforderungsänderungen additiv gefertigter Produkte}}},
  doi          = {{https://www.researchgate.net/publication/331742431_Methode_zur_Bewertung_von_Anforderungsanderungen_additiv_gefertigter_Produkte}},
  volume       = {{29}},
  year         = {{2018}},
}

@proceedings{10591,
  editor       = {{Abiteboul, S. and Arenas, M. and Barceló, P. and Bienvenu, M. and Calvanese, D. and David, C. and Hull, R. and Hüllermeier, Eyke and Kimelfeld, B. and Libkin, L. and Martens, W. and Milo, T. and Murlak, F. and Neven, F. and Ortiz, M. and Schwentick, T. and Stoyanovich, J. and Su, J. and Suciu, D. and Vianu, V. and Yi, K.}},
  number       = {{1}},
  pages        = {{1--29}},
  title        = {{{Research Directions for Principles of Data Management}}},
  volume       = {{7}},
  year         = {{2018}},
}

@inbook{10783,
  author       = {{Couso, Ines and Hüllermeier, Eyke}},
  booktitle    = {{Frontiers in Computational Intelligence}},
  editor       = {{Mostaghim, Sanaz and Nürnberger, Andreas and Borgelt, Christian}},
  pages        = {{31--46}},
  publisher    = {{Springer}},
  title        = {{{Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators}}},
  year         = {{2018}},
}

@article{16038,
  author       = {{Schäfer, D. and Hüllermeier, Eyke}},
  journal      = {{Machine Learning}},
  number       = {{5}},
  pages        = {{903--941}},
  title        = {{{Dyad ranking using Plackett-Luce models based on joint feature representations}}},
  volume       = {{107}},
  year         = {{2018}},
}

@inproceedings{10145,
  author       = {{Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}},
  booktitle    = {{Proc. 32 nd AAAI Conference on Artificial Intelligence (AAAI)}},
  pages        = {{2951--2958}},
  title        = {{{Learning to Rank Based on Analogical Reasoning}}},
  year         = {{2018}},
}

@inproceedings{10148,
  author       = {{El Mesaoudi-Paul, Adil and Hüllermeier, Eyke and Busa-Fekete, Robert}},
  booktitle    = {{Proc. 35th Int. Conference on Machine Learning (ICML)}},
  pages        = {{3469--3477}},
  publisher    = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}},
  title        = {{{Ranking Distributions based on Noisy Sorting}}},
  year         = {{2018}},
}