Karlson Pfannschmidt

Cited by

	All	Since 2018
Citations	145	132
h-index	4	4
i10-index	3	3

201620172018201920202021202220231 12 14 21 22 35 27 13

Public access

View all

1 article

0 articles

available

not available

Based on funding mandates

Co-authors

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
Christian OffenUniversität PaderbornVerified email at uni-paderborn.de
Michael DellnitzProfessor for Applied Mathematics, Paderborn UniversityVerified email at uni-paderborn.de
Sebastian PeitzAssistant Professor for "Data Science for Engineering", Paderborn UniversityVerified email at upb.de
Sina Ober-BlöbaumUniversity of OxfordVerified email at eng.ox.ac.uk
Marvin LückeZuse Institute BerlinVerified email at zib.de
Alexander TornedeLeibniz University HannoverVerified email at ai.uni-hannover.de

Karlson Pfannschmidt

Paderborn University

Verified email at mail.upb.de - Homepage

Machine Learning Preference Learning Bayesian Optimization Choice Modeling


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	109	2016
Deep architectures for learning context-dependent ranking functions K Pfannschmidt, P Gupta, E Hüllermeier arXiv preprint arXiv:1803.05796, 2018	12	2018
Learning context-dependent choice functions K Pfannschmidt, P Gupta, B Haddenhorst, E Hüllermeier International Journal of Approximate Reasoning 140, 116-155, 2022	10*	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	8	2016
Learning Choice Functions via Pareto-Embeddings K Pfannschmidt, E Hüllermeier KI 2020: Advances in Artificial Intelligence: 43rd German Conference on AI …, 2020	3	2020
Efficient time stepping for numerical integration using reinforcement learning M Lücke, M Dellnitz, E Hüllermeier, S Ober-Blöbaum, C Offen, S Peitz, ... SIAM Journal on Scientific Computing, 2021	2*	2021
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
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
A Characterization of Choice Functions Representable by Pareto-Embedding of Alternatives K Pfannschmidt, H Eyke
Shapley Curves: A New Concept for Modelling Feature Importance F Adnan, K Pfannschmidt, E Hüllermeier

The system can't perform the operation now. Try again later.

Articles 1–10

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow

Cited by

Co-authors