16 citations · 16 across the 5 of their papers we have counts for
5 papers
Fragments to Facts: Partial-Information Fragment Inference from LLMs
Lucas Rosenblatt, Bin Han, Robert Wolfe +1
Large language models (LLMs) can leak sensitive training data through memorization and membership inference attacks. Prior work has primarily focused on strong adversarial assumpti…
Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives
Lucas Rosenblatt, Bill Howe, Julia Stoyanovich
Data privacy is a core tenet of responsible computing, and in the United States, differential privacy (DP) is the dominant technical operationalization of privacy-preserving data a…
Sharper Bounds for Chebyshev Moment Matching, with Applications
Cameron Musco, Christopher Musco, Lucas Rosenblatt +1
We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. This problem arises broadly across algorit…
Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings
Robert Wolfe, Isaac Slaughter, Bin Han +8
The rapid proliferation of generative AI has raised questions about the competitiveness of lower-parameter, locally tunable, open-weight models relative to high-parameter, API-guar…
Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology
Lucas Rosenblatt, Bin Han, Erin Posthumus +2
An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States. We tackle the problem of predicting buffel…