Fred Lu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 30

Top Topics

Logistic Regression

Bregman Divergences

Top Venues

Publications

Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. KDD (2024)
Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification. CoRR (2024)
Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. CoRR (2024)
Amol Khanna, Fred Lu, Edward Raff, Brian Testa
Differentially Private Logistic Regression with Sparse Solutions. AISec@CCS (2023)
Edward Raff, Amol Khanna, Fred Lu
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations. CoRR (2023)
Fred Lu, Edward Raff, Francis Ferraro
Neural Bregman Divergences for Distance Learning. ICLR (2023)
Amol Khanna, Fred Lu, Edward Raff, Brian Testa
Sparse Private LASSO Logistic Regression. CoRR (2023)
Fred Lu, Edward Raff, James Holt
A Coreset Learning Reality Check. AAAI (2023)
Tyler LeBlond, Joseph Munoz, Fred Lu, Maya Fuchs, Elliott Zaresky-Williams, Edward Raff, Brian Testa
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile. CoRR (2023)
Amol Khanna, Fred Lu, Edward Raff
The Challenge of Differentially Private Screening Rules. CoRR (2023)
Tyler LeBlond, Joseph Munoz, Fred Lu, Maya Fuchs, Elliott Zaresky-Williams, Edward Raff, Brian Testa
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile. AISec@CCS (2023)
Fred Lu, Edward Raff, James Holt
A Coreset Learning Reality Check. CoRR (2023)
Tirth Patel, Fred Lu, Edward Raff, Charles Nicholas, Cynthia Matuszek, James Holt
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits! CoRR (2023)
Edward Raff, Amol Khanna, Fred Lu
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations. NeurIPS (2023)
Tirth Patel, Fred Lu, Edward Raff, Charles Nicholas, Cynthia Matuszek, James Holt
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits! CAMLIS (2023)
Skyler Wu, Fred Lu, Edward Raff, James Holt
Exploring the Sharpened Cosine Similarity. CoRR (2023)
Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa
A General Framework for Auditing Differentially Private Machine Learning. CoRR (2022)
Pablo M. De Salazar, Fred Lu, James A. Hay, Diana Gomez-Barroso, Pablo Fernández-Navarro, Elena V. Martínez, Jenaro Astray-Mochales, Rocío Amillategui, Ana García-Fulgueiras, Maria D. Chirlaque, Alonso Sánchez-Migallón, Amparo Larrauri, María J. Sierra, Marc Lipsitch, Fernando Simón, Mauricio Santillana, Miguel A. Hernán
Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data. PLoS Comput. Biol. 18 (3) (2022)
Fred Lu, Francis Ferraro, Edward Raff
Continuously Generalized Ordinal Regression for Linear and Deep Models. CoRR (2022)
Peyman Hosseinzadeh Kassani, Fred Lu, Yann Le Guen, Michaël E. Belloy, Zihuai He
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants. Nat. Mac. Intell. 4 (9) (2022)
Fred Lu, Edward Raff, Francis Ferraro
Neural Bregman Divergences for Distance Learning. CoRR (2022)
André T. Nguyen, Richard Zak, Luke E. Richards, Maya Fuchs, Fred Lu, Robert Brandon, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt
Minimizing Compute Costs: When Should We Run More Expensive Malware Analysis? CAMLIS (2022)
André T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks. AAAI (2022)
Fred Lu, Francis Ferraro, Edward Raff
Continuously Generalized Ordinal Regression for Linear and Deep Models. SDM (2022)
Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa
A General Framework for Auditing Differentially Private Machine Learning. NeurIPS (2022)
André T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks. CoRR (2022)
Peyman Hosseinzadeh Kassani, Fred Lu, Yann Le Guen, Zihuai He
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants. CoRR (2021)
Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. ICLR (2021)
Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Stefano Ermon
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. CoRR (2020)
Shihao Yang, Samuel C. Kou, Fred Lu, John S. Brownstein, Nicholas Brooke, Mauricio Santillana
Advances in using Internet searches to track dengue. PLoS Comput. Biol. 13 (7) (2017)