Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy, AISTATS 2023 (Oral Presentation)
Women in Machine Learning Workshop (WIML-NeurIPS-2022)
Optimal Accounting of Differential Privacy via Characteristic Function, AISTATS 2022
CSS Theory and Practice of Differential Privacy Workshop (TPDP-2021)
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE, AISTATS 2022
Voting-based Approaches For Differentially Private Federated Learning, International Workshop on Federated Learning (FL-NeurIPS-2022)
Improving Sparse Vector Technique with Renyi Differential Privacy, NeurIPS 2020
CSS Theory and Practice of Differential Privacy Workshop (TPDP-2020)
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning, Journal of Machine Learning Research (JMLR-2022), shorter version appeared in AISTATS 2021
Model-Agnostic Private Learning with Domain Adaptation, CSS Theory and Practice of Differential Privacy Workshop (TPDP-2020)
Private-kNN: Practical Differential Privacy for Computer Vision, CVPR 2020
Ph.D. in Computer Science from UC Santa Barbara, advised by Prof. Yu-Xiang Wang; B.S. in Computer Science from Nanjing University, advised by Prof. Wu-Jun LI
Background
Research Interests: Machine learning, including differential privacy, domain adaptation, and federated learning. Recently focusing on establishing rigorous differential privacy guarantees for large-scale real datasets. Also working on an open-source project, Auto DP, which aims to help researchers obtain provable DP guarantees with advanced techniques in differential privacy.