Fred Lu
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.

Fred Lu

Publications

2023

  1. F. Lu, E. Raff, and F. Ferraro, "Neural Bregman Divergences for Distance Learning", InProceedings, 11th International Conference on Learning Representations, May 2023, 106 downloads.

2022

  1. 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, 211 downloads.
  2. 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, 215 downloads.