Karlson Pfannschmidt
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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
Deep architectures for learning context-dependent ranking functions
K Pfannschmidt, P Gupta, E Hüllermeier
arXiv preprint arXiv:1803.05796, 2018
Learning context-dependent choice functions
K Pfannschmidt, P Gupta, B Haddenhorst, E Hüllermeier
International Journal of Approximate Reasoning 140, 116-155, 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
Learning Choice Functions via Pareto-Embeddings
K Pfannschmidt, E Hüllermeier
KI 2020: Advances in Artificial Intelligence: 43rd German Conference on AI …, 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
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
scikit-optimize/scikit-optimize: High five - v0.5
T Head, MechCoder, G Louppe, I Shcherbatyi, fcharras, Z Vinícius, ..., 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
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