arXiv:2510.06028v3 Announce Type: replace Abstract: This paper provides data-dependent bounds on the expected error of the Gibbs algorithm in the overparameterized interpolation regime, where low training errors are also obtained for impossible data, such as random labels in classification. The results show that generalization in the low-temperature regime is already signaled by small training errors in the noisier high-temperature regime. The bounds are stable under approximation with Langevin Monte Carlo algorithms. The analysis motivates the design of an algorithm to compute bounds, which o
This research provides theoretical advancements in understanding generalization within complex AI models, a critical area of focus as AI systems become more ubiquitous and are deployed in sensitive applications.
Improved theoretical understanding of AI generalization, particularly in overparameterized regimes, can lead to more robust, reliable, and trustworthy AI systems, expanding their applicability.
The ability to bound expected errors and understand generalization in high-temperature regimes provides new pathways for designing more predictable and auditable AI models.
- · AI researchers
- · AI developers
- · High-stakes AI applications (e.g., medical, finance)
- · Developers of 'black box' AI models
- · Traditional statistical modeling approaches
The findings can lead to more stable and interpretable training algorithms for deep learning models.
Increased trust in AI systems could accelerate their adoption in critical infrastructure and decision-making processes.
A deeper theoretical grounding for AI generalization might reduce the need for extensive empirical testing, streamlining development cycles.
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Read at arXiv cs.LG