Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers

• URL: http://arxiv.org/abs/2605.07892v2
• Date: Fri, 15 May 2026 16:44:04 GMT
• Title: Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers
• Abstract summary: We propose an adaptive regularization scheme that updates $$ based on the difference between the model's current sparsity and the target sparsity.<n>The proposed method reliably achieves sparsity targets ranging between 75% and 99%.<n>It also converges faster than the oracle-tuned non-adaptive baseline during early training and matches or surpasses its final performance in equal error rate.
• Score: 14.98763942493771
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 updates $λ$ based on the difference between the model's current sparsity and the target sparsity. We analyze the resulting algorithm and evaluate it on automatic speaker verification with ECAPA-TDNN and ResNet34 on VoxCeleb and CNCeleb. The proposed method reliably achieves sparsity targets ranging between 75% and 99%. It also converges faster than the oracle-tuned non-adaptive baseline during early training and matches or surpasses its final performance in equal error rate. We further show that the adaptive scheme inherits key properties from its non-adaptive counterpart, including improved out-of-distribution robustness over the dense baselines.

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