Renewable Lasso without Batch-Number Constraints: A Gradient-Enhanced Approach
We study online estimation for high-dimensional generalized linear models with streaming data. First, for the non-distributed setting, we propose a gradient-enhanced surrogate loss that approximates the cumulative loss using only historical summaries, which modifies and improves upon the existing renewable estimation approach for the same model in the high-dimensional setting, and removes the batch-number constraint in previous studies. We then extend the method to distributed streaming data under the master-client architecture, where batches are partitioned across sites and only summaries (gradient vectors) are exchanged. Instead of directing applying the popular method of Jordan et al. ...
| Authors | Junzhuo Gao, Ling Peng, Xu Guo et al. |
|---|---|
| Primary category | stat.ML |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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