1 citations · 1 across the 4 of their papers we have counts for
4 papers
Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models
Jing Wang, HaiYing Wang, Qiang Zhang +1
Subsampling is effective in tackling computational challenges for massive data with rare events. Overly aggressive subsampling may adversely affect estimation efficiency, and optim…
Robust Data Fusion via Subsampling
Jing Wang, HaiYing Wang, Kun Chen
Data fusion and transfer learning are rapidly growing fields that enhance model performance for a target population by leveraging other related data sources or tasks. The challenge…
Unweighted estimation based on optimal sample under measurement constraints
Jing Wang, HaiYing Wang, Shifeng Xiong
To tackle massive data, subsampling is a practical approach to select the more informative data points. However, when responses are expensive to measure, developing efficient subsa…
Sampling with replacement vs Poisson sampling: a comparative study in optimal subsampling
Jing Wang, Jiahui Zou, HaiYing Wang
Faced with massive data, subsampling is a commonly used technique to improve computational efficiency, and using nonuniform subsampling probabilities is an effective approach to im…