Hermite Polynomial Features for Private Data Generation

Vinaroz  , Margarita; Charusaie  , Mohammad-Amin; Harder, Frederik; Adamczewski, Kamil; Park, Mi Jung

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Hermite Polynomial Features for Private Data Generation

MPS-Authors

Vinaroz , Margarita
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

Charusaie , Mohammad-Amin
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Harder, Frederik
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Adamczewski, Kamil
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://proceedings.mlr.press/v162/vinaroz22a.html
(Publisher version)

https://doi.org/10.48550/arXiv.2106.05042
(Preprint)

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Citation

Vinaroz, M., Charusaie, M.-A., Harder, F., Adamczewski, K., & Park, M. J. (2022). Hermite Polynomial Features for Private Data Generation. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, & S. Sabato (Eds.), Proceedings of the 39th International Conference on Machine Learning (ICML 2022) (pp. 22300-22324). PMLR. Retrieved from https://proceedings.mlr.press/v162/vinaroz22a.html.

Cite as: https://hdl.handle.net/21.11116/0000-0010-2F7E-E

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