3 papers
cs.CL2024
Dr. SoW: Density Ratio of Strong-over-weak LLMs for Reducing the Cost of Human Annotation in Preference Tuning
Guangxuan Xu, Kai Xu, Shivchander Sudalairaj +2
Preference tuning relies on high-quality human preference data, which is often expensive and time-consuming to gather. In this paper, we introduce Dr.SoW (Density Ratio of Strong o…
cs.LG2024
Differentially Private Synthetic Data Generation for Relational Databases
Kaveh Alimohammadi, Hao Wang, Ojas Gulati +2
Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with…
cs.CR2023
Private Synthetic Data Meets Ensemble Learning
Haoyuan Sun, Navid Azizan, Akash Srivastava +1
When machine learning models are trained on synthetic data and then deployed on real data, there is often a performance drop due to the distribution shift between synthetic and rea…