Lu, Fred

10 publications

ECML-PKDD 2025 Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt

NeurIPS 2024 Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling Skyler Wu, Fred Lu, Edward Raff, James Holt

AAAI 2023 A Coreset Learning Reality Check Fred Lu, Edward Raff, James Holt

ICLR 2023 Neural Bregman Divergences for Distance Learning Fred Lu, Edward Raff, Francis Ferraro

NeurIPS 2023 Scaling up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations Edward Raff, Amol Khanna, Fred Lu

ICMLW 2023 The Challenge of Differentially Private Screening Rules Amol Khanna, Fred Lu, Edward Raff

NeurIPS 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

NeurIPSW 2022 Exploring the Sharpened Cosine Similarity Skyler Wu, Fred Lu, Edward Raff, James Holt

AAAI 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

ICLR 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