1 citations · 1 across the 8 of their papers we have counts for
5 papers · 1 filter
Risk-Equalized Differentially Private Synthetic Data: Protecting Outliers by Controlling Record-Level Influence
Amir Asiaee, Chao Yan, Zachary B. Abrams +1
When synthetic data is released, some individuals are harder to protect than others. A patient with a rare disease combination or a transaction with unusual characteristics stands…
PRISM: Differentially Private Synthetic Data with Structure-Aware Budget Allocation for Prediction
Amir Asiaee, Chao Yan, Zachary B. Abrams +1
Differential privacy (DP) provides a mathematical guarantee limiting what an adversary can learn about any individual from released data. However, achieving this protection typical…
Private Prediction via Shrinkage
Chao Yan
We study differentially private prediction introduced by Dwork and Feldman (COLT 2018): an algorithm receives one labeled sample set and then answers a stream of unlabeled quer…
An ptimal Differentially Private Learner for Concept Classes with VC Dimension 1
Chao Yan
We present the first nearly optimal differentially private PAC learner for any concept class with VC dimension 1 and Littlestone dimension . Our algorithm achieves the sample co…
AlignedKV: Reducing Memory Access of KV-Cache with Precision-Aligned Quantization
Yifan Tan, Haoze Wang, Chao Yan +1
Model quantization has become a crucial technique to address the issues of large memory consumption and long inference times associated with LLMs. Mixed-precision quantization, whi…