Hermite Polynomial Features for Private Data Generation
Authors: Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mi Jung Park
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As demonstrated on several tabular and image datasets, Hermite polynomial features seem better suited for private data generation than random Fourier features. |
| Researcher Affiliation | Academia | 1Max Planck Institute for Intelligent Systems, Tuebingen, Germany 2University of British Columbia, Vancouver, Canada. CIFAR AI Chair at AMII. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ParkLabML/DP-HP. |
| Open Datasets | Yes | The dataset is publicly available at the UCI machine learning repository at the following link: https://archive.ics.uci.edu/ml/datasets/adult. The Census dataset is also a public dataset that can be downloaded via the SDGym package 9. and We follow previous work in testing our method on image datasets MNIST (Le Cun et al., 2010) (license: CC BY-SA 3.0) and Fashion MNIST (Xiao et al., 2017) (license: MIT). |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test splits for all datasets. For the Gaussian mixture, it mentions "reserving 10% for the test set, which yields 90000 training samples", but no specific validation split is described for any dataset. |
| Hardware Specification | Yes | Our experiments were implemented in Py Torch (Paszke et al., 2019) and run using Nvidia Kepler20 and Kepler80 GPUs. |
| Software Dependencies | Yes | Models are taken from the scikit-learn 0.24.2 and xgboost 0.90 python packages |
| Experiment Setup | Yes | For the experimental setup of DP-HP on the image datasets see Table 9 in Appendix Sec. H.2. Table 9 lists hyperparameters such as "Hermite order (sum kernel) 100", "gamma 5", "mini-batch size 200", "epochs 10", "learning rate 0.01". |