6.3k citations · 7.3k across the 9 of their papers we have counts for
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Oneshot Differentially Private Top-k Selection
Gang Qiao, Weijie J. Su, Li Zhang
Being able to efficiently and accurately select the top- elements with differential privacy is an integral component of various private data analysis tasks. In this paper, we pr…
Rényi Differential Privacy of the Sampled Gaussian Mechanism
Ilya Mironov, Kunal Talwar, Li Zhang
The Sampled Gaussian Mechanism (SGM)---a composition of subsampling and the additive Gaussian noise---has been successfully used in a number of machine learning applications. The m…
Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar +1
We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy. Our…
Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry
Kunal Talwar, Abhradeep Thakurta, Li Zhang
Empirical Risk Minimization (ERM) is a standard technique in machine learning, where a model is selected by minimizing a loss function over constraint set. When the training datase…