An adaptive regularization update for Bregman optimizers achieves target sparsity levels from 75% to 99% with faster early convergence and performance matching or exceeding oracle-tuned baselines.
Self- tuning networks: Bilevel optimization of hyperparameters using structured best-response func- tions
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Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers
An adaptive regularization update for Bregman optimizers achieves target sparsity levels from 75% to 99% with faster early convergence and performance matching or exceeding oracle-tuned baselines.