Online Generalized Sparse Regression: How Does Overparametrization Help?
Regularized sparse regression has been extensively studied in the offline setting, but online formulation remains relatively under-explored. This gap stems from four key challenges: (i) the infeasibility of dynamically updating the regularization parameter in every online round, (ii) managing storage and memory complexity, (iii) enabling real-time computation via closed-form updates rather than solving full optimization problems at each round, and (iv) achieving optimal statistical guarantees under realistic assumptions. In this paper, we propose an online generalized-sparsity-constrained regression framework, focusing on online cardinality-constrained linear regression and low-rank matri...
| Authors | Shuoguang Yang, Qiang Sun |
|---|---|
| Primary category | stat.ML |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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