Advances in using Internet searches to track dengue. - Shihao Yang 002; Samuel C. Kou; Fred Lu; John S. Brownstein; Nicholas Brooke; Mauricio Santillana (PLoS Comput. Biol., 2017)

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. - Sharon Zhou; Eric Zelikman; Fred Lu; Andrew Y. Ng; Stefano Ermon (CoRR, 2020)

Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. - Sharon Zhou; Eric Zelikman; Fred Lu; Andrew Y. Ng; Gunnar E. Carlsson; Stefano Ermon (ICLR, 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)

Out of Distribution Data Detection Using Dropout Bayesian Neural Networks. - André T. Nguyen; Fred Lu; Gary Lopez Munoz; Edward Raff; Charles Nicholas; James Holt (AAAI, 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 (CAMLIS, 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 (NeurIPS, 2022)

Continuously Generalized Ordinal Regression for Linear and Deep Models. - Fred Lu; Francis Ferraro; Edward Raff (SDM, 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; et al. (PLoS Comput. Biol., 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., 2022)

A Coreset Learning Reality Check. - Fred Lu; Edward Raff; James Holt (AAAI, 2023)

Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits! - Tirth Patel; Fred Lu; Edward Raff; Charles Nicholas; Cynthia Matuszek; James Holt (CAMLIS, 2023)

Differentially Private Logistic Regression with Sparse Solutions. - Amol Khanna; Fred Lu; Edward Raff; Brian Testa (AISec@CCS, 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 (AISec@CCS, 2023)

Neural Bregman Divergences for Distance Learning. - Fred Lu; Edward Raff; Francis Ferraro (ICLR, 2023)

Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations. - Edward Raff; Amol Khanna; Fred Lu (NeurIPS, 2023)

The Challenge of Differentially Private Screening Rules. - Amol Khanna; Fred Lu; Edward Raff (CoRR, 2023)

Sparse Private LASSO Logistic Regression. - Amol Khanna; Fred Lu; Edward Raff; Brian Testa (CoRR, 2023)

Exploring the Sharpened Cosine Similarity. - Skyler Wu; Fred Lu; Edward Raff; James Holt (CoRR, 2023)

Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection. - Siddhant Gupta; Fred Lu; Andrew Barlow; Edward Raff; Francis Ferraro; Cynthia Matuszek; Charles Nicholas; James Holt (IEEE Big Data, 2024)

High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. - Fred Lu; Ryan R. Curtin; Edward Raff; Francis Ferraro; James Holt (KDD, 2024)

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

Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification. - Fred Lu; Ryan R. Curtin; Edward Raff; Francis Ferraro; James Holt (CoRR, 2024)

Differentially Private Iterative Screening Rules for Linear Regression. - Amol Khanna; Fred Lu; Edward Raff (CODASPY, 2025)

Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms. - Derek Everett; Fred Lu; Edward Raff; Fernando Camacho; James Holt (CoRR, 2025)