28 citations · 325 across the 73 of their papers we have counts for
43 papers · 1 filter
Private Data Stream Analysis for Universal Symmetric Norm Estimation
Vladimir Braverman, Joel Manning, Zhiwei Steven Wu +1
We study how to release summary statistics on a data stream subject to the constraint of differential privacy. In particular, we focus on releasing the family of symmetric norms, w…
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
Jingfeng Wu, Wennan Zhu, Peter Kairouz +1
In federated frequency estimation (FFE), multiple clients work together to estimate the frequencies of their collective data by communicating with a server that respects the privac…
Lower Bounds for Pseudo-Deterministic Counting in a Stream
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan +1
Many streaming algorithms provide only a high-probability relative approximation. These two relaxations, of allowing approximation and randomization, seem necessary -- for many str…
The Power of Uniform Sampling for Coresets
Vladimir Braverman, Vincent Cohen-Addad, Shaofeng H. -C. Jiang +4
Motivated by practical generalizations of the classic -median and -means objectives, such as clustering with size constraints, fair clustering, and Wasserstein barycenter, we…
The White-Box Adversarial Data Stream Model
Miklos Ajtai, Vladimir Braverman, T. S. Jayram +4
We study streaming algorithms in the white-box adversarial model, where the stream is chosen adaptively by an adversary who observes the entire internal state of the algorithm at e…
Symmetric Norm Estimation and Regression on Sliding Windows
Vladimir Braverman, Viska Wei, Samson Zhou
The sliding window model generalizes the standard streaming model and often performs better in applications where recent data is more important or more accurate than data that arri…