arXiv:2605.26000v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is a foundational algorithm for large-scale statistical learning and stochastic optimization. However, statistical inference based on SGD iterates remains challenging when stochastic gradients have infinite variance, as the relevant limiting distributions depend on unknown nuisance parameters. In this paper, we develop an efficient, model-agnostic methodology for constructing confidence regions from SGD trajectories that applies in both finite- and infinite-variance regimes. The procedure is based on a joint we
The continuous scaling of AI models and data processing increasingly relies on robust and reliable statistical inference techniques, especially as computational methods push established statistical boundaries.
Improving the reliability of statistical inference for large-scale AI models enhances the trustworthiness and generalizability of AI systems, crucial for deployment in sensitive applications.
This research provides a more robust methodology for understanding and validating the statistical properties of AI models trained with stochastic gradient descent, moving beyond previous limitations.
- · AI researchers
- · Large-scale AI deployments
- · High-stakes AI applications
- · Statistical learning theory
- · Ad-hoc AI validation methods
Increased confidence in the statistical guarantees of advanced AI models.
Faster adoption of AI in fields requiring rigorous validation, such as finance, medicine, and critical infrastructure.
Further acceleration of AI development as researchers can more rapidly iterate on robust and explainable models.
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