A generalized variance-reduced ZO hard-thresholding algorithm removes prior limits on random directions for gradient estimates, yielding improved convergence rates under standard assumptions.
Proceedings of the 10th ACM workshop on artificial intelligence and security , pages=
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Zeroth-order optimization is underexplored rather than underpowered in deep learning, with limitations stemming from full-space designs that can be addressed via subspace, spectral, and systems-aware approaches.
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New Insight of Variance reduce in Zero-Order Hard-Thresholding: Mitigating Gradient Error and Expansivity Contradictions
A generalized variance-reduced ZO hard-thresholding algorithm removes prior limits on random directions for gradient estimates, yielding improved convergence rates under standard assumptions.
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Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered
Zeroth-order optimization is underexplored rather than underpowered in deep learning, with limitations stemming from full-space designs that can be addressed via subspace, spectral, and systems-aware approaches.