8 citations · 10 across the 7 of their papers we have counts for
7 papers
Private Adaptive Covariance Estimation via Gaussian Graphical Models
Cecilia Ferrando, Miguel Fuentes, Brett Mullins +2
We propose PACE-GGM, a data-adaptive differentially private method for covariance estimation that concentrates its privacy budget on the most informative entries of the empirical c…
Fast Private Adaptive Query Answering for Large Data Domains
Miguel Fuentes, Brett Mullins, Yingtai Xiao +3
Privately releasing marginals of a tabular dataset is a foundational problem in differential privacy. However, state-of-the-art mechanisms suffer from a computational bottleneck wh…
Efficient and Private Marginal Reconstruction with Local Non-Negativity
Brett Mullins, Miguel Fuentes, Yingtai Xiao +3
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…
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
Miguel Fuentes, Brett Mullins, Ryan McKenna +2
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…
The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning
Brett Mullins
Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE,…
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
Ryan McKenna, Brett Mullins, Daniel Sheldon +1
We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a se…
Identifying the Most Explainable Classifier
Brett Mullins
We introduce the notion of pointwise coverage to measure the explainability properties of machine learning classifiers. An explanation for a prediction is a definably simple region…