3 papers
cs.DS2026
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…
cs.LG2025
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…
cs.HC2024
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…