Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers
Sparse training reduces the memory and computational costs of deep neural networks. However, sparse optimization methods, e.g., those adding an $\ell_1$ penalty, often control sparsity only indirectly through a regularization parameter $λ$, whose mapping to the final sparsity rate is non-trivial. In our experiments, we found this parameter sensitivity to be particularly pronounced for Bregman-based optimizers. Specifically, the two variants LinBreg and AdaBreg reach the same sparsity at $λ$ values that differ by up to two orders of magnitude, requiring expensive trial-and-error sweeps to achieve a user-specified sparsity. To address this, we propose an adaptive regularization scheme that ...
| Authors | Ahmad Aloradi, Tim Roith, Emanuël A. P. Habets et al. |
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| Primary category | cs.LG |
| Published | 8 May 2026 |
| Last updated at source | 20 May 2026 |
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