Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models
Subsampling is effective in tackling computational challenges for massive data with rare events. Overly aggressive subsampling may adversely affect estimation efficiency, and optimal subsampling is essential to mitigate the information loss. However, existing optimal subsampling probabilities depend on data scales, and some scaling transformations may result in inefficient subsamples. This problem is more significant when there are inactive features, because their influence on the subsampling probabilities can be arbitrarily magnified by inappropriate scaling transformations. We tackle this challenge and introduce a scale-invariant optimal subsampling function in the context of sparse mod...
| Authors | Jing Wang, HaiYing Wang, Qiang Zhang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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