Fred Lu

42 papers A* 9A 1B 1Journal 24Unranked 7
YearRankTypeTitle / Venue / Authors
2026 J jnl
Adversarial Samples Are Not Created Equal.
CoRR
Jennifer Crawford, Amol Khanna, Fred Lu, Amy R. Wagoner, Stella Biderman, André T. Nguyen, Edward Raff
2026 A* conf
Intermediate N-Gramming: Deterministic and Fast N-Grams for Large N and Large Datasets.
AAAI
Ryan R. Curtin, Fred Lu, Edward Raff, Priyanka Ranade
2025 — conf
Differentially Private Iterative Screening Rules for Linear Regression.
CODASPY
Amol Khanna, Fred Lu, Edward Raff
2025 J jnl
Differentially Private Iterative Screening Rules for Linear Regression.
CoRR
Amol Khanna, Fred Lu, Edward Raff
2025 J jnl
Intermediate N-Gramming: Deterministic and Fast N-Grams For Large N and Large Datasets.
CoRR
Ryan R. Curtin, Fred Lu, Edward Raff, Priyanka Ranade
2025 — conf
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification.
ECML/PKDD (5)
Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
2025 — conf
Quick Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms.
KDD (2)
Derek Everett, Fred Lu, Edward Raff, Fernando Camacho, James Holt
2025 J jnl
Quick-Draw Bandits: Quickly Optimizing in Nonstationary Environments with Extremely Many Arms.
CoRR
Derek Everett, Fred Lu, Edward Raff, Fernando Camacho, James Holt
2024 A* conf
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates.
KDD
Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
2024 J jnl
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates.
CoRR
Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
2024 B conf
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection.
IEEE Big Data
Siddhant Gupta, Fred Lu, Andrew Barlow, Edward Raff, Francis Ferraro, Cynthia Matuszek, Charles Nicholas, James Holt
2024 J jnl
Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection.
CoRR
Siddhant Gupta, Fred Lu, Andrew Barlow, Edward Raff, Francis Ferraro, Cynthia Matuszek, Charles Nicholas, James Holt
2024 J jnl
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification.
CoRR
Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt
2024 A* conf
Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling.
NeurIPS
Skyler Wu, Fred Lu, Edward Raff, James Holt
2024 J jnl
Stabilizing Linear Passive-Aggressive Online Learning with Weighted Reservoir Sampling.
CoRR
Skyler Wu, Fred Lu, Edward Raff, James Holt
2023 A* conf
A Coreset Learning Reality Check.
AAAI
Fred Lu, Edward Raff, James Holt
2023 J jnl
A Coreset Learning Reality Check.
CoRR
Fred Lu, Edward Raff, James Holt
2023 — conf
Differentially Private Logistic Regression with Sparse Solutions.
AISec@CCS
Amol Khanna, Fred Lu, Edward Raff, Brian Testa
2023 J jnl
Exploring the Sharpened Cosine Similarity.
CoRR
Skyler Wu, Fred Lu, Edward Raff, James Holt
2023 A* conf
Neural Bregman Divergences for Distance Learning.
ICLR
Fred Lu, Edward Raff, Francis Ferraro
2023 — conf
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile.
AISec@CCS
Tyler LeBlond, Joseph Munoz, Fred Lu, Maya Fuchs, Elliott Zaresky-Williams, Edward Raff, Brian Testa
2023 J jnl
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile.
CoRR
Tyler LeBlond, Joseph Munoz, Fred Lu, Maya Fuchs, Elliott Zaresky-Williams, Edward Raff, Brian Testa
2023 A* conf
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations.
NeurIPS
Edward Raff, Amol Khanna, Fred Lu
2023 J jnl
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations.
CoRR
Edward Raff, Amol Khanna, Fred Lu
2023 — conf
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!
CAMLIS
Tirth Patel, Fred Lu, Edward Raff, Charles Nicholas, Cynthia Matuszek, James Holt
2023 J jnl
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!
CoRR
Tirth Patel, Fred Lu, Edward Raff, Charles Nicholas, Cynthia Matuszek, James Holt
2023 J jnl
Sparse Private LASSO Logistic Regression.
CoRR
Amol Khanna, Fred Lu, Edward Raff, Brian Testa
2023 J jnl
The Challenge of Differentially Private Screening Rules.
CoRR
Amol Khanna, Fred Lu, Edward Raff
2022 A* conf
A General Framework for Auditing Differentially Private Machine Learning.
NeurIPS
Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa
2022 J jnl
A General Framework for Auditing Differentially Private Machine Learning.
CoRR
Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott Zaresky-Williams, Edward Raff, Francis Ferraro, Brian Testa
2022 A conf
Continuously Generalized Ordinal Regression for Linear and Deep Models.
SDM
Fred Lu, Francis Ferraro, Edward Raff
2022 J jnl
Continuously Generalized Ordinal Regression for Linear and Deep Models.
CoRR
Fred Lu, Francis Ferraro, Edward Raff
2022 J jnl
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants.
Nat. Mac. Intell.
Peyman Hosseinzadeh Kassani, Fred Lu, Yann Le Guen, Michaël E. Belloy, Zihuai He
2022 — conf
Minimizing Compute Costs: When Should We Run More Expensive Malware Analysis?
CAMLIS
André T. Nguyen, Richard Zak, Luke E. Richards, Maya Fuchs, Fred Lu, Robert Brandon, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt
2022 J jnl
Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data.
PLoS Comput. Biol.
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
2022 J jnl
Neural Bregman Divergences for Distance Learning.
CoRR
Fred Lu, Edward Raff, Francis Ferraro
2022 A* conf
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks.
AAAI
André T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt
2022 J jnl
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks.
CoRR
André T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt
2021 J jnl
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants.
CoRR
Peyman Hosseinzadeh Kassani, Fred Lu, Yann Le Guen, Zihuai He
2021 A* conf
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology.
ICLR
Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Gunnar E. Carlsson, Stefano Ermon
2020 J jnl
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology.
CoRR
Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Stefano Ermon
2017 J jnl
Advances in using Internet searches to track dengue.
PLoS Comput. Biol.
Shihao Yang, Samuel C. Kou, Fred Lu, John S. Brownstein, Nicholas Brooke, Mauricio Santillana