Publications

Everything from 2024 onward. Titles link to the best available PDF; older work is on Google Scholar.

2026

  1. Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
    Lucas Rosenblatt, Peihan Liu, Ryan McKenna, Natalia Ponomareva
    International Conference on Machine Learning (ICML) 2026
  2. Exactly Computing do-Shapley Values
    R. Teal Witter, Álvaro Parafita, Tomas Garriga, Maximilian Muschalik, Fabian Fumagalli, Axel Brando, Lucas Rosenblatt
    International Conference on Machine Learning (ICML) 2026
  3. Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
    Lucas Rosenblatt, Bernease Herman, Anastasia Holovenko, Wonkwon Lee, Joshua R. Loftus, Elizabeth McKinnie, Taras Rumezhak, Andrii Stadnik, Bill Howe, Julia Stoyanovich
    Communications of the ACM, 69(7), 2026 · CACM Research Highlight
  4. How to DP-Fy Your Data: A Practical Guide to Generating Synthetic Data with Differential Privacy
    Natalia Ponomareva, Zheng Xu, H. Brendan McMahan, Peter Kairouz, Lucas Rosenblatt, Vincent Cohen-Addad, Cristóbal Guzmán, Ryan McKenna, Galen Andrew, Alex Bie, Da Yu, Alex Kurakin, Morteza Zadimoghaddam, Sergei Vassilvitskii, Andreas Terzis
    Journal of Artificial Intelligence Research (JAIR), 86, 2026
  5. ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
    Peihan Liu, Lucas Rosenblatt, Weiwei Kong, Natalia Ponomareva, Gautam Kamath, Rachel Cummings, Roxana Geambasu, Yu Gan, Lillian Tsai, Alex Bie
    arXiv preprint, 2026
  6. Towards Provably Unbiased LLM Judges via Bias-Bounded Evaluation
    Benjamin Feuer, Lucas Rosenblatt, Oussama Elachqar
    arXiv preprint, 2026
  7. Explanation Multiplicity in SHAP: Characterization and Assessment
    Hyunseung Hwang, Seungeun Lee, Lucas Rosenblatt, Steven Euijong Whang, Julia Stoyanovich
    arXiv preprint, 2026
  8. Trustworthy Differentially Private Data Generation and Predictive Modeling
    Lucas Rosenblatt
    PhD dissertation, New York University, 2026

2025

  1. Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
    Shlomi Hod, Lucas Rosenblatt, Julia Stoyanovich
    NeurIPS 2025 (Datasets & Benchmarks Track)
  2. Sharper Bounds for Chebyshev Moment Matching, with Applications to Differential Privacy and Beyond
    Cameron Musco, Christopher Musco, Lucas Rosenblatt, Apoorv Vikram Singh
    Conference on Learning Theory (COLT) 2025
  3. Fragments to Facts: Partial-Information Fragment Inference from LLMs
    Lucas Rosenblatt, Bin Han, Robert Wolfe, Bill Howe
    International Conference on Machine Learning (ICML) 2025
  4. Differential Privacy Under Class Imbalance: Methods and Empirical Insights
    Lucas Rosenblatt, Yuliia Lut, Eitan Turok, Marco Avella-Medina, Rachel Cummings
    International Conference on Machine Learning (ICML) 2025
  5. SimKey: A Semantically Aware Key Module for Watermarking Language Models
    Shingo Kodama, Haya Diwan, Lucas Rosenblatt, R. Teal Witter, Niv Cohen
    arXiv preprint, 2025

2024

  1. A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy
    Lucas Rosenblatt, Julia Stoyanovich, Christopher Musco
    AAAI 2024
  2. I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets Initiatives
    R. Teal Witter, Lucas Rosenblatt
    AAAI 2024
  3. Laboratory-Scale AI: Open-Weight Models are Competitive with ChatGPT Even in Low-Resource Settings
    Robert Wolfe, Isaac Slaughter, Bin Han, Bingbing Wen, Yiwei Yang, Lucas Rosenblatt, Bernease Herman, Eva Maxfield Brown, Zening Qu, Nic Weber, Bill Howe
    ACM Conference on Fairness, Accountability, and Transparency (FAccT) 2024
  4. Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy
    Lucas Rosenblatt, Bernease Herman, Anastasia Holovenko, Wonkwon Lee, Joshua R. Loftus, Elizabeth McKinnie, Taras Rumezhak, Andrii Stadnik, Bill Howe, Julia Stoyanovich
    SIGMOD Record, 53(1), 2024 · SIGMOD Research Highlight
  5. FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairness
    Lucas Rosenblatt, R. Teal Witter
    arXiv preprint, 2024
  6. Are Data Experts Buying into Differentially Private Synthetic Data? Gathering Community Perspectives
    Lucas Rosenblatt, Bill Howe, Julia Stoyanovich
    arXiv preprint, 2024

Publications before 2024 are listed on my Google Scholar profile.