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

2. Optimizing the Optimal Weighted Average: Efficient Distributed Sparse Classification

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

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

5. Exploring the Sharpened Cosine Similarity

6. Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile

7. Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations

8. Sparse Private LASSO Logistic Regression

9. The Challenge of Differentially Private Screening Rules

10. A Coreset Learning Reality Check

11. A General Framework for Auditing Differentially Private Machine Learning

12. Neural Bregman Divergences for Distance Learning

13. Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

14. Continuously Generalized Ordinal Regression for Linear and Deep Models

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

16. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

17. An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

18. Evaluating the Disentanglement of Deep Generative Models through Manifold Topology

19. Sub-national levels and trends in contraceptive prevalence, unmet need, and demand for family planning in Nigeria with survey uncertainty

21. Advances in using Internet searches to track dengue

23. Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations

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

34. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

35. Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches

36. Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

37. Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

38. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

39. Estimating the Prevalence of COVID-19 in the United States: Three Complementary Approaches

48. Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis

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