Privately Publishable Per-instance Privacy. - Rachel Redberg; Yu-Xiang Wang 003 (NeurIPS, 2021)

Tree++: Truncated Tree Based Graph Kernels. - Wei Ye 001; Zhen Wang; Rachel Redberg; Ambuj K. Singh (IEEE Trans. Knowl. Data Eng., 2021)

Differentially Private Linear Sketches: Efficient Implementations and Applications. - Fuheng Zhao; Dan Qiao 002; Rachel Redberg; Divyakant Agrawal; Amr El Abbadi; Yu-Xiang Wang 003 (NeurIPS, 2022)

Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy. - Rachel Redberg; Yuqing Zhu 005; Yu-Xiang Wang 003 (AISTATS, 2023)

Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners. - Rachel Redberg; Antti Koskela; Yu-Xiang Wang 003 (NeurIPS, 2023)

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. - Yingyu Lin; Yian Ma; Yu-Xiang Wang 003; Rachel Redberg (CoRR, 2023)

Noise-adding and beyond: A study in data-adaptive methods for differential privacy - Rachel Redberg (2023)

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy. - Yingyu Lin; Yian Ma; Yu-Xiang Wang 003; Rachel Redberg; Zhiqi Bu (ICLR, 2024)

Privacy Profiles for Private Selection. - Antti Koskela; Rachel Redberg; Yu-Xiang Wang 003 (ICML, 2024)