UMBC ebiquity
Fred Lu
Booz Allen Hamilton
Fred Lu works on machine learning research at Booz Allen Hamilton with applications in adversarial defense, privacy, and cyber. I am also involved with biostatistics, genomics, and computational epidemiology research at Stanford and Harvard Universities. Currently, he is also pursuing a part-time Ph.D. in CS/ML at UMBC.
Publications
2023
- F. Lu, E. Raff, and F. Ferraro, "Neural Bregman Divergences for Distance Learning", InProceedings, 11th International Conference on Learning Representations, May 2023, 160 downloads.
2022
- F. Lu, J. Munoz, M. Fuchs, T. LeBlond, E. Zaresky-Williams, E. Raff, F. Ferraro, and B. Testa, "A General Framework for Auditing Differentially Private Machine Learning", InProceedings, Advances in Neural Information Processing Systems, November 2022, 298 downloads.
- F. Lu, F. Ferraro, and E. Raff, "Continuously Generalized Ordinal Regression for Linear and Deep Models", InProceedings, SIAM International Conference on Data Mining, April 2022, 281 downloads.