An Approach to Supervised Learning of Three-Valued Łukasiewicz Logic in Hölldobler's Core Method. - Frederik Harder; Tarek Richard Besold (AIC, 2016)

Learning Łukasiewicz logic. - Frederik Harder; Tarek R. Besold (Cogn. Syst. Res., 2018)

DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning. - Frederik Harder; Jonas Köhler 001; Max Welling; Mijung Park (CoRR, 2019)

Interpretable and Differentially Private Predictions. - Frederik Harder; Matthias Bauer; Mijung Park (AAAI, 2020)

Differentially Private Mean Embeddings with Random Features (DP-MERF) for Simple & Practical Synthetic Data Generation. - Frederik Harder; Kamil Adamczewski; Mijung Park (CoRR, 2020)

Q-FIT: The Quantifiable Feature Importance Technique for Explainable Machine Learning. - Kamil Adamczewski; Frederik Harder; Mijung Park (CoRR, 2020)

DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation. - Frederik Harder; Kamil Adamczewski; Mijung Park (AISTATS, 2021)

Polynomial magic! Hermite polynomials for private data generation. - Mijung Park; Margarita Vinaroz; Mohammad-Amin Charusaie; Frederik Harder (CoRR, 2021)

Hermite Polynomial Features for Private Data Generation. - Margarita Vinaroz; Mohammad-Amin Charusaie; Frederik Harder; Kamil Adamczewski; Mijung Park (ICML, 2022)

Differentially Private Data Generation Needs Better Features. - Frederik Harder; Milad Jalali Asadabadi; Danica J. Sutherland; Mijung Park (CoRR, 2022)

Pre-trained Perceptual Features Improve Differentially Private Image Generation. - Frederik Harder; Milad Jalali; Danica J. Sutherland; Mijung Park (Trans. Mach. Learn. Res., 2023)