Frederik Harder
Frederik Harder
Max Planck Institute for Intelligent Systems & University of Tübingen & International Max Planck Research School for Intelligent Systems (IMPRS-IS)
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Interpretable and Differentially Private Predictions.
F Harder, M Bauer, M Park
AAAI, 4083-4090, 2020
Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple & Practical Synthetic Data Generation
F Harder, K Adamczewski, M Park
arXiv preprint arXiv:2002.11603, 2020
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
Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning
K Adamczewski, F Harder, M Park
arXiv preprint arXiv:2010.13872, 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
Learning Łukasiewicz logic
F Harder, TR Besold
Cognitive Systems Research 47, 42-67, 2018
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