Search Results for author: Frederik Harder

Found 5 papers, 4 papers with code

Hermite Polynomial Features for Private Data Generation

1 code implementation9 Jun 2021 Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park

Hence, a relatively low order of Hermite polynomial features can more accurately approximate the mean embedding of the data distribution compared to a significantly higher number of random features.

Bayesian Importance of Features (BIF)

no code implementations26 Oct 2020 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.

Bayesian Inference BIG-bench Machine Learning +2

DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data Generation

1 code implementation26 Feb 2020 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 of synthetic data.

Privacy Preserving Synthetic Data Generation

DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning

1 code implementation15 Oct 2019 Frederik Harder, Jonas Köhler, Max Welling, Mijung Park

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 train deep neural networks.

Interpretable and Differentially Private Predictions

1 code implementation5 Jun 2019 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 points.

General Classification

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