From the 1 of 9 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
9 papers
Fixed-Parameter Tractability of Private Synthetic Data Generation
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3
The paper investigates generating differentially private synthetic data and shows that the problem is fixed-parameter tractable when parameterized by the treewidth of the query fam…
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…
Tracking solutions of time-varying variational inequalities
Hédi Hadiji, Sarah Sachs, Cristóbal Guzmán
Tracking the solution of time-varying variational inequalities is an important problem with applications in game theory, optimization, and machine learning. Existing work considers…
Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
Mario Bravo, Juan P. Flores-Mella, Cristóbal Guzmán
We study the mixing time of the projected Langevin algorithm (LA) and the privacy curve of noisy Stochastic Gradient Descent (SGD), beyond nonexpansive iterations. Specifically, we…
Computational Hardness of Private Coreset
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath +3
We study the problem of differentially private (DP) computation of coreset for the -means objective. For a given input set of points, a coreset is another set of points such tha…
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Tomás González, Cristóbal Guzmán, Courtney Paquette
We study the problem of differentially-private (DP) stochastic (convex-concave) saddle-points in the setting. We propose -DP algorithms based on stochas…