1 citations · 1 across the 2 of their papers we have counts for
4 papers
Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
Lucas Rosenblatt, Peihan Liu, Ryan McKenna +1
Research on differentially private synthetic tabular data has largely focused on independent and identically distributed rows where each record corresponds to a unique individual.…
Benchmarking Differentially Private Tabular Data Synthesis
Kai Chen, Xiaochen Li, Chen Gong +2
Differentially private (DP) tabular data synthesis generates artificial data that preserves the statistical properties of private data while safeguarding individual privacy. The em…
Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
Marika Swanberg, Ryan McKenna, Edo Roth +2
Differentially private (DP) synthetic data is a versatile tool for enabling the analysis of private data. Recent advancements in large language models (LLMs) have inspired a number…
Learn from the Past: Language-conditioned Object Rearrangement with Large Language Models
Guanqun Cao, Ryan Mckenna, Erich Graf +1
Object manipulation for rearrangement into a specific goal state is a significant task for collaborative robots. Accurately determining object placement is a key challenge, as misa…