6 citations · 26 across the 22 of their papers we have counts for
6 papers · 1 filter
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12
High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…
Approximating High-Dimensional Earth Mover's Distance as Fast as Closest Pair
Lorenzo Beretta, Vincent Cohen-Addad, Rajesh Jayaram +1
We give a reduction from -approximate Earth Mover's Distance (EMD) to -approximate Closest Pair (CP). As a consequence, we improve the fastest kno…
Scalable contribution bounding to achieve privacy
Vincent Cohen-Addad, Alessandro Epasto, Jason Lee +1
In modern datasets, where single records can have multiple owners, enforcing user-level differential privacy requires capping each user's total contribution. This "contribution bou…
Private Training & Data Generation by Clustering Embeddings
Felix Zhou, Samson Zhou, Vahab Mirrokni +2
Deep neural networks often use large, high-quality datasets to achieve high performance on many machine learning tasks. When training involves potentially sensitive data, this proc…
Efficient Data Selection at Scale via Influence Distillation
Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh +1
Effective data selection is critical for efficient training of modern Large Language Models (LLMs). This paper introduces Influence Distillation, a novel, mathematically-justified…
Scalable Private Partition Selection via Adaptive Weighting
Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto +1
In the differentially private partition selection problem (a.k.a. private set union, private key discovery), users hold subsets of items from an unbounded universe. The goal is to…