most citedFaking feature importance: A cautionary tale on the use of differentially-private synthetic data

5 citations · 7 across the 3 of their papers we have counts for

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cs.LG2022★ 5 cited

Faking feature importance: A cautionary tale on the use of differentially-private synthetic data

Oscar Giles, Kasra Hosseini, Grigorios Mingas +13

Synthetic datasets are often presented as a silver-bullet solution to the problem of privacy-preserving data publishing. However, for many applications, synthetic data has been sho…

cs.LG2020★ 2 cited

Foundations of Bayesian Learning from Synthetic Data

Harrison Wilde, Jack Jewson, Sebastian Vollmer +1

There is significant growth and interest in the use of synthetic data as an enabler for machine learning in environments where the release of real data is restricted due to privacy…