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arXiv Machine Learning By Lucas Rosenblatt, R. Teal Witter, Christopher Musco

Differentially Private Synthetic Data from Sliced Chebyshev Moments

Read the original on arXiv Machine Learning →

The paper introduces SPLICE, a differentially private synthetic data algorithm that uses noisy sliced Chebyshev polynomial moments to fit a distribution. SPLICE achieves a Sliced Wasserstein-1 distance of ∼O(1/√n) from the target distribution, a bound that does not depend on the data dimension. The authors also prove a new moment‑matching result and provide a practical implementation that outperforms existing DP synthetic data methods on a large benchmark.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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