arXiv:2508.20326v2 Announce Type: replace-cross Abstract: Stochastic gradient optimization is the dominant learning paradigm for a variety of scenarios, from classical supervised learning to modern self-supervised learning. We consider stochastic gradient algorithms for learning problems whose objectives rely on unknown nuisance parameters, and establish non-asymptotic convergence guarantees. Our results show that, while the presence of a nuisance can alter the optimum and upset the optimization trajectory, the classical stochastic gradient algorithm may still converge under appropriate condit
This paper addresses a fundamental challenge in AI optimization, building upon decades of research in stochastic gradients to refine core learning paradigms.
Improved understanding and robustness of stochastic gradient optimization directly impacts the reliability and performance of AI/ML systems across all applications, from classical to advanced self-supervised learning.
The theoretical guarantees for stochastic gradient algorithms will become more robust, especially when dealing with unknown nuisance parameters, leading to more stable and predictable AI model development.
- · AI/ML researchers
- · Cloud AI providers
- · Companies using AI for critical applications
- · AI models without robust optimization
- · Competitors reliant on less stable algorithms
More efficient and reliable training of complex AI models.
Reduced computational costs and faster iteration cycles for AI development.
Acceleration of new AI applications that currently struggle with optimization stability.
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Read at arXiv cs.LG