Karlson Pfannschmidt

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Eyke HüllermeierProfessor of Computer Science, Paderborn UniversityVerified email at upb.de
Pritha GuptaUniversität PaderbornVerified email at mail.upb.de
Kalina Jasinska-KobusPoznan University of TechnologyVerified email at cs.put.poznan.pl
Timo KlerxResearch Assistant, University of PaderbornVerified email at upb.de
Krzysztof DembczyńskiPoznan University of TechnologyVerified email at cs.put.poznan.pl
Robert Busa-FeketeResearch scientist, Google ResearchVerified email at google.com
Marvin LückeZuse Institute BerlinVerified email at zib.de
Christian OffenUniversität PaderbornVerified email at uni-paderborn.de
Michael DellnitzProfessor for Applied Mathematics, Paderborn UniversityVerified email at uni-paderborn.de
Sebastian PeitzProfessor of Safe Autonomous Systems, TU DortmundVerified email at tu-dortmund.de
Sina Ober-BlöbaumUniversity of OxfordVerified email at eng.ox.ac.uk
Alexander TornedeBeckhoff AutomationVerified email at beckhoff.com

Karlson Pfannschmidt

Paderborn University

Verified email at mail.upb.de - Homepage

Machine Learning Preference Learning Bayesian Optimization Choice Modeling


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Extreme F-Measure Maximization using Sparse Probability Estimates K Jasinska, K Dembczynski, R Busa-Fekete, K Pfannschmidt, T Klerx, ... International Conference on Machine Learning 33, 1435-1444, 2016	130	2016
Learning context-dependent choice functions K Pfannschmidt, P Gupta, B Haddenhorst, E Hüllermeier International Journal of Approximate Reasoning 140, 116-155, 2022	18*	2022
Evaluating tests in medical diagnosis: combining machine learning with game-theoretical concepts K Pfannschmidt, E Hüllermeier, S Held, R Neiger Information Processing and Management of Uncertainty in Knowledge-Based …, 2016	15	2016
Deep architectures for learning context-dependent ranking functions K Pfannschmidt, P Gupta, E Hüllermeier arXiv preprint arXiv:1803.05796, 2018	14	2018
Efficient time-stepping for numerical integration using reinforcement learning M Dellnitz, E Hüllermeier, M Lücke, S Ober-Blöbaum, C Offen, S Peitz, ... SIAM Journal on Scientific Computing 45 (2), A579-A595, 2023	6	2023
Learning choice functions via pareto-embeddings K Pfannschmidt, E Hüllermeier KI 2020: Advances in Artificial Intelligence: 43rd German Conference on AI …, 2020	6	2020
jPL: A java-based software framework for preference learning P Gupta, A Hetzer, T Tornede, S Gottschalk, A Kornelsen, S Osterbrink, ... Proceedings of the LWDA, 2017	1	2017
A Prototype to Support Business Model Innovation through Crowdsourcing and Artificial Intelligence A Klippenstein, C Weskamp, F Laux, F Neuhaus, K Pfannschmidt, ... Wirtschaftsinformatik 2023 47, 2023		2023
A Characterization of Choice Functions Representable by Pareto-Embedding of Alternatives K Pfannschmidt, E Hüllermeier DA2PL 2022, 2022		2022
scikit-optimize/scikit-optimize: High five - v0.5 T Head, MechCoder, G Louppe, I Shcherbatyi, fcharras, Z Vinícius, ... https://doi.org/10.5281/zenodo.1165540, 2018		2018
Shapley Curves: A New Concept for Modelling Feature Importance F Adnan, K Pfannschmidt, E Hüllermeier LWDA 2017, 2017		2017

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