2 citations · 2 across the 5 of their papers we have counts for
7 papers
PE-means: Improved Differentially Private -means Clustering through Private Evolution
Thomas Humphries, Zinan Lin, Sergey Yekhanin
We study the problem of differentially private (DP) -means clustering in Euclidean space. Previous solutions rely on summing the private data directly, which induces a sensitivi…
Differentially Private Synthetic Data via APIs 4: Tabular Data
Toan Tran, Arturs Backurs, Zinan Lin +3
This paper investigates the problem of generating synthetic tabular data with differential privacy (DP) guarantees, enabling data sharing in sensitive domains. Despite extensive st…
Struct-Bench: A Benchmark for Differentially Private Structured Text Generation
Shuaiqi Wang, Vikas Raunak, Arturs Backurs +7
Differentially private (DP) synthetic data generation is a promising technique for utilizing private datasets that otherwise cannot be exposed for model training or other analytics…
Latent Zoning Network: A Unified Principle for Generative Modeling, Representation Learning, and Classification
Zinan Lin, Enshu Liu, Xuefei Ning +3
Generative modeling, representation learning, and classification are three core problems in machine learning (ML), yet their state-of-the-art (SoTA) solutions remain largely disjoi…
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
Zinan Lin, Tadas Baltrusaitis, Wenyu Wang +1
Differentially private (DP) synthetic data enables the use of sensitive datasets while providing strong privacy guarantees. Recently, Private Evolution (PE) has emerged as a promis…
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
Enshu Liu, Junyi Zhu, Zinan Lin +8
Diffusion Models (DM) and Consistency Models (CM) are two types of popular generative models with good generation quality on various tasks. When training DM and CM, intermediate we…
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs +9
Text data has become extremely valuable due to the emergence of machine learning algorithms that learn from it. A lot of high-quality text data generated in the real world is priva…