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Frederik Harder
Frederik Harder
Max Planck Institute for Intelligent Systems & University of Tübingen & International Max Planck
Verified email at tuebingen.mpg.de
Title
Cited by
Cited by
Year
DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation
F Harder, K Adamczewski, M Park
International Conference on Artificial Intelligence and Statistics, 1819-1827, 2021
33*2021
Interpretable and differentially private predictions
F Harder, M Bauer, M Park
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4083-4090, 2020
262020
Differentially Private Data Generation Needs Better Features
F Harder, MJ Asadabadi, DJ Sutherland, M Park
arXiv preprint arXiv:2205.12900, 2022
22022
Learning Łukasiewicz logic
F Harder, TR Besold
Cognitive Systems Research 47, 42-67, 2018
22018
Hermite Polynomial Features for Private Data Generation
M Vinaroz, MA Charusaie, F Harder, K Adamczewski, MJ Park
International Conference on Machine Learning, 22300-22324, 2022
12022
An approach to supervised learning of three valued Lukasiewicz logic in Hölldobler's core method
F Harder, TR Besold
CEUR Workshop Proceedings 1895, 24-37, 2017
12017
Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning
K Adamczewski, F Harder, M Park
arXiv preprint arXiv:2010.13872, 2020
2020
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
F Harder, J Köhler, M Welling, M Park
arXiv preprint arXiv:1910.06924, 2019
2019
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