Identifying the Most Explainable Classifier. - Brett Mullins (CoRR, 2019)

AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. - Ryan McKenna; Brett Mullins; Daniel Sheldon; Gerome Miklau (Proc. VLDB Endow., 2022)

The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning. - Brett Mullins (CoRR, 2023)

Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. - Miguel Fuentes; Brett C. Mullins; Ryan McKenna; Gerome Miklau; Daniel Sheldon (AISTATS, 2024)

Efficient and Private Marginal Reconstruction with Local Non-Negativity. - Brett Mullins; Miguel Fuentes; Yingtai Xiao; Daniel Kifer; Cameron Musco; Daniel R. Sheldon (NeurIPS, 2024)