| 2026 | AAAI | Intermediate N-Gramming: Deterministic and Fast N-Grams for Large N and Large Datasets. | Ryan R. Curtin, Fred Lu, Edward Raff, Priyanka Ranade |
| 2025 | KDD | Quick Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms. | Derek Everett, Fred Lu, Edward Raff, Fernando Camacho, James Holt |
| 2024 | KDD | High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. | Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt |
| 2023 | AAAI | A Coreset Learning Reality Check. | Fred Lu, Edward Raff, James Holt |
| 2023 | CCS | Differentially Private Logistic Regression with Sparse Solutions. | Amol Khanna, Fred Lu, Edward Raff, Brian Testa |
| 2023 | CCS | Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile. | Tyler LeBlond, Joseph Munoz, Fred Lu, Maya Fuchs, Elliott Zaresky-Williams, Edward Raff, Brian Testa |
| 2023 | ICLR | Neural Bregman Divergences for Distance Learning. | Fred Lu, Edward Raff, Francis Ferraro |
| 2022 | AAAI | Out of Distribution Data Detection Using Dropout Bayesian Neural Networks. | Andr T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt |
| 2022 | SDM | Continuously Generalized Ordinal Regression for Linear and Deep Models. | Fred Lu, Francis Ferraro, Edward Raff |
| 2021 | ICLR | Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. | Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon |