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20222026
most citedNoise-Aware Statistical Inference with Differentially Private Synthetic Data

2 citations · 3 across the 8 of their papers we have counts for

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stat.ML2024

Subsampling is not Magic: Why Large Batch Sizes Work for Differentially Private Stochastic Optimisation

Ossi Räisä, Joonas Jälkö, Antti Honkela

We study how the batch size affects the total gradient variance in differentially private stochastic gradient descent (DP-SGD), seeking a theoretical explanation for the usefulness…

stat.ML2022★ 2 cited

Noise-Aware Statistical Inference with Differentially Private Synthetic Data

Ossi Räisä, Joonas Jälkö, Samuel Kaski +1

While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less.…