1 citations · 2 across the 5 of their papers we have counts for
5 papers
Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls
Aras Selvi, Eleonora Kreacic, Mohsen Ghassemi +3
Adversarially robust optimization (ARO) has emerged as the *de facto* standard for training models that hedge against adversarial attacks in the test stage. While these models are…
Guarding Multiple Secrets: Enhanced Summary Statistic Privacy for Data Sharing
Shuaiqi Wang, Rongzhe Wei, Mohsen Ghassemi +2
Data sharing enables critical advances in many research areas and business applications, but it may lead to inadvertent disclosure of sensitive summary statistics (e.g., means or q…
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Rongzhe Wei, Eleonora Kreačić, Haoyu Wang +4
Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue. Although prior studies have performed empirical ev…
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
Mufei Li, Eleonora Kreačić, Vamsi K. Potluru +1
Large-scale graphs with node attributes are increasingly common in various real-world applications. Creating synthetic, attribute-rich graphs that mirror real-world examples is cru…
Differentially Private Synthetic Data Using KD-Trees
Eleonora Kreačić, Navid Nouri, Vamsi K. Potluru +2
Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge. Many space partitioning based a…