arXiv:2605.24513v1 Announce Type: new Abstract: This paper considers the nonconvex nonsmooth problem in which the objective function is Lipschitz continuous. We focus on the stochastic setting where the algorithm can access stochastic function value evaluations with heavy-tailed noise, which is prevalent in many popular machine learning applications. We propose a stochastic zeroth-order algorithm that refines the framework of online-to-nonconvex conversion by clipping the two-point gradient estimator. The theoretical analysis shows that our algorithm can find a $(\delta, \epsilon)$-Goldstein s
This research addresses a fundamental challenge in machine learning optimization, particularly in the stochastic setting with heavy-tailed noise, which is prevalent in real-world large-scale AI applications.
Improved optimization algorithms are critical for advancing AI capabilities and efficiency, directly impacting the development and deployment of more robust and reliable AI systems.
The proposed zeroth-order algorithm provides a more stable and efficient method for optimizing nonconvex, nonsmooth problems under challenging noise conditions, potentially enabling new ML applications and improving existing ones.
- · AI researchers
- · Machine learning developers
- · Companies deploying AI models
- · Researchers using less robust optimization methods
More efficient training for complex AI models in noisy environments becomes possible.
This could accelerate the development of more sophisticated AI agents and autonomous systems.
Improved fundamental AI capabilities might contribute to breakthroughs across various AI-driven sectors, reducing computational burdens.
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Read at arXiv cs.LG