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Journal Articles
- 2022
[j3]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): 761-771 (2022)- [j2]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) - 2017
- [j1]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)
Conference and Workshop Papers
- 2024
- [c12]Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt:
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. KDD 2024: 2037-2047 - 2023
- [c11]Fred Lu, Edward Raff, James Holt:
A Coreset Learning Reality Check. AAAI 2023: 8940-8948 - [c10]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: 181-192 - [c9]Amol Khanna, Fred Lu, Edward Raff, Brian Testa:
Differentially Private Logistic Regression with Sparse Solutions. AISec@CCS 2023: 1-9 - [c8]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: 23-33 - [c7]Fred Lu, Edward Raff, Francis Ferraro:
Neural Bregman Divergences for Distance Learning. ICLR 2023 - [c6]Edward Raff, Amol Khanna, Fred Lu:
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations. NeurIPS 2023 - 2022
- [c5]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: 7877-7885 - [c4]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: 81-99 - [c3]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 - [c2]Fred Lu, Francis Ferraro, Edward Raff:
Continuously Generalized Ordinal Regression for Linear and Deep Models. SDM 2022: 28-36 - 2021
- [c1]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
Informal and Other Publications
- 2024
- [i15]Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt:
Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification. CoRR abs/2406.01753 (2024) - [i14]Fred Lu, Ryan R. Curtin, Edward Raff, Francis Ferraro, James Holt:
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. CoRR abs/2407.06346 (2024) - 2023
- [i13]Fred Lu, Edward Raff, James Holt:
A Coreset Learning Reality Check. CoRR abs/2301.06163 (2023) - [i12]Amol Khanna, Fred Lu, Edward Raff:
The Challenge of Differentially Private Screening Rules. CoRR abs/2303.10303 (2023) - [i11]Amol Khanna, Fred Lu, Edward Raff, Brian Testa:
Sparse Private LASSO Logistic Regression. CoRR abs/2304.12429 (2023) - [i10]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 abs/2306.15790 (2023) - [i9]Skyler Wu, Fred Lu, Edward Raff, James Holt:
Exploring the Sharpened Cosine Similarity. CoRR abs/2307.13855 (2023) - [i8]Edward Raff, Amol Khanna, Fred Lu:
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations. CoRR abs/2310.19978 (2023) - [i7]Tirth Patel, Fred Lu, Edward Raff, Charles Nicholas, Cynthia Matuszek, James Holt:
Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits! CoRR abs/2312.15813 (2023) - 2022
- [i6]Fred Lu, Francis Ferraro, Edward Raff:
Continuously Generalized Ordinal Regression for Linear and Deep Models. CoRR abs/2202.07005 (2022) - [i5]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 abs/2202.08985 (2022) - [i4]Fred Lu, Edward Raff, Francis Ferraro:
Neural Bregman Divergences for Distance Learning. CoRR abs/2206.04763 (2022) - [i3]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 abs/2210.08643 (2022) - 2021
- [i2]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 abs/2109.14719 (2021) - 2020
- [i1]Sharon Zhou, Eric Zelikman, Fred Lu, Andrew Y. Ng, Stefano Ermon:
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology. CoRR abs/2006.03680 (2020)
Coauthor Index
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last updated on 2024-09-10 02:08 CEST by the dblp team
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