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Journal Articles
- 2022
[j1]Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau:
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. Proc. VLDB Endow. 15(11): 2599-2612 (2022)
Conference and Workshop Papers
- 2024
- [c2]Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. AISTATS 2024: 2404-2412 - [c1]Brett Mullins, Miguel Fuentes, Yingtai Xiao, Daniel Kifer, Cameron Musco, Daniel R. Sheldon:
Efficient and Private Marginal Reconstruction with Local Non-Negativity. NeurIPS 2024
Informal and Other Publications
- 2024
- [i5]Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon:
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. CoRR abs/2403.07797 (2024) - [i4]Brett Mullins, Miguel Fuentes, Yingtai Xiao, Daniel Kifer, Cameron Musco, Daniel Sheldon:
Efficient and Private Marginal Reconstruction with Local Non-Negativity. CoRR abs/2410.01091 (2024) - 2023
- [i3]Brett Mullins:
The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning. CoRR abs/2301.09042 (2023) - 2022
- [i2]Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau:
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. CoRR abs/2201.12677 (2022) - 2019
- [i1]Brett Mullins:
Identifying the Most Explainable Classifier. CoRR abs/1910.08595 (2019)
Coauthor Index
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