Fred Lu

According to our database, Fred Lu authored at least 22 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling.

Skyler Wu, Fred Lu, Edward Raff, James Holt

CoRR, 2024

Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification.

Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt

CoRR, 2024

High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates.

Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023

Exploring the Sharpened Cosine Similarity.

Skyler Wu, Fred Lu, Edward Raff, James Holt

CoRR, 2023

Sparse Private LASSO Logistic Regression.

Amol Khanna, Fred Lu, Edward Raff, Brian Testa

CoRR, 2023

The Challenge of Differentially Private Screening Rules.

Amol Khanna, Fred Lu, Edward Raff

CoRR, 2023

Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations.

Edward Raff, Amol Khanna, Fred Lu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Bregman Divergences for Distance Learning.

Fred Lu, Edward Raff, Francis Ferraro

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023

Differentially Private Logistic Regression with Sparse Solutions.

Amol Khanna, Fred Lu, Edward Raff, Brian Testa

Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023

Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!

Tirth Patel, Fred Lu, Edward Raff, Charles Nicholas, Cynthia Matuszek, James Holt

Proceedings of the Conference on Applied Machine Learning in Information Security, 2023

A Coreset Learning Reality Check.

Fred Lu, Edward Raff, James Holt

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants.

Peyman Hosseinzadeh Kassani, Fred Lu, Yann Le Guen, Michaël E. Belloy, Zihuai He

Nat. Mac. Intell., September, 2022

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data.

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

PLoS Comput. Biol., 2022

Continuously Generalized Ordinal Regression for Linear and Deep Models.

Fred Lu, Francis Ferraro, Edward Raff

Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

A General Framework for Auditing Differentially Private Machine Learning.

Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Minimizing Compute Costs: When Should We Run More Expensive Malware Analysis?

André T. Nguyen, Richard Zak, Luke E. Richards, Maya Fuchs, Fred Lu, Robert Brandon, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt

Proceedings of the Conference on Applied Machine Learning in Information Security, 2022

Out of Distribution Data Detection Using Dropout Bayesian Neural Networks.

André T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Deep neural networks with controlled variable selection for the identification of putative causal genetic variants.

Peyman Hosseinzadeh Kassani, Fred Lu, Yann Le Guen, Zihuai He

CoRR, 2021

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology.

Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology.

Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Stefano Ermon

CoRR, 2020

2017

Advances in using Internet searches to track dengue.

Shihao Yang, Samuel C. Kou, Fred Lu, John S. Brownstein, Nicholas Brooke, Mauricio Santillana

PLoS Comput. Biol., 2017