Miguel Fuentes — accepted AI-conference papers

2024

Efficient and Private Marginal Reconstruction with Local Non-Negativity

Brett Mullins, Miguel Fuentes, Yingtai Xiao, Daniel Kifer, Cameron N Musco, Daniel Sheldon

NeurIPS 2024poster

Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private algorithms for query release and synthetic data contain steps that reconstruct answers to queries from answers to other…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2024

Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data

Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon

AISTATS 2024poster

Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on pu…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗