23 citations · 26 across the 4 of their papers we have counts for
5 papers
Hermite Polynomial Features for Private Data Generation
Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder +2
Kernel mean embedding is a useful tool to represent and compare probability measures. Despite its usefulness, kernel mean embedding considers infinite-dimensional features, which a…
Bayesian Importance of Features (BIF)
Kamil Adamczewski, Frederik Harder, Mijung Park
We introduce a simple and intuitive framework that provides quantitative explanations of statistical models through the probabilistic assessment of input feature importance. The co…
DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation
Frederik Harder, Kamil Adamczewski, Mijung Park
We propose a differentially private data generation paradigm using random feature representations of kernel mean embeddings when comparing the distribution of true data with that o…
DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning
Frederik Harder, Jonas Köhler, Max Welling +1
Developing a differentially private deep learning algorithm is challenging, due to the difficulty in analyzing the sensitivity of objective functions that are typically used to tra…
Interpretable and Differentially Private Predictions
Frederik Harder, Matthias Bauer, Mijung Park
Interpretable predictions, where it is clear why a machine learning model has made a particular decision, can compromise privacy by revealing the characteristics of individual data…