3 citations · 5 across the 9 of their papers we have counts for
10 papers
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
Kareem Amin, Rudrajit Das, Alessandro Epasto +4
The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets. Ho…
AI-rithmetic
Alex Bie, Travis Dick, Alex Kulesza +3
Modern AI systems have been successfully deployed to win medals at international math competitions, assist with research workflows, and prove novel technical lemmas. However, despi…
Learning from Synthetic Data: Limitations of ERM
Kareem Amin, Alex Bie, Weiwei Kong +2
The prevalence and low cost of LLMs have led to a rise of synthetic content. From review sites to court documents, "natural" content has been contaminated by data points that appea…
How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy
Natalia Ponomareva, Zheng Xu, H. Brendan McMahan +12
High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated da…
Differentially Private Synthetic Data Release for Topics API Outputs
Travis Dick, Alessandro Epasto, Adel Javanmard +5
The analysis of the privacy properties of Privacy-Preserving Ads APIs is an area of research that has received strong interest from academics, industry, and regulators. Despite thi…
An Optimization Framework for Differentially Private Sparse Fine-Tuning
Mehdi Makni, Kayhan Behdin, Gabriel Afriat +5
Differentially private stochastic gradient descent (DP-SGD) is broadly considered to be the gold standard for training and fine-tuning neural networks under differential privacy (D…
Escaping Collapse: The Strength of Weak Data for Large Language Model Training
Kareem Amin, Sara Babakniya, Alex Bie +3
Synthetically-generated data plays an increasingly larger role in training large language models. However, while synthetic data has been found to be useful, studies have also shown…
Practical Considerations for Differential Privacy
Kareem Amin, Alex Kulesza, Sergei Vassilvitskii
Differential privacy is the gold standard for statistical data release. Used by governments, companies, and academics, its mathematically rigorous guarantees and worst-case assumpt…
Private prediction for large-scale synthetic text generation
Kareem Amin, Alex Bie, Weiwei Kong +5
We present an approach for generating differentially private synthetic text using large language models (LLMs), via private prediction. In the private prediction framework, we only…
Scaling Laws for Downstream Task Performance of Large Language Models
Berivan Isik, Natalia Ponomareva, Hussein Hazimeh +3
Scaling laws provide important insights that can guide the design of large language models (LLMs). Existing work has primarily focused on studying scaling laws for pretraining (ups…