arXiv:2510.17303v2 Announce Type: replace Abstract: Symmetries are known to improve the empirical performance of machine learning models, yet theoretical guarantees explaining these gains remain limited. Prior work has focused mainly on compact group symmetries and often assumes that the data distribution itself is invariant, an assumption rarely satisfied in real-world applications. In this work, we extend generalization guarantees to the broader setting of non-compact symmetries, such as translations and to non-invariant data distributions. Building on the PAC-Bayes framework, we adapt and t
This academic paper extends theoretical guarantees in PAC-Bayesian learning, reflecting ongoing fundamental research in AI, specifically machine learning generalization.
While relevant to AI researchers, this specific advancement in theoretical guarantees for machine learning models is incremental and unlikely to have immediate strategic implications for a broader audience.
The theoretical understanding of how symmetries affect machine learning generalization is slightly refined, moving beyond compact group symmetries and invariant data distributions.
- · Machine Learning Researchers
- · Deep Learning Theory
Refines theoretical frameworks for understanding robustness and generalization in AI models.
May contribute to the development of more robust or sample-efficient AI systems in the distant future.
Potentially informs new architectural designs for neural networks that better leverage diverse symmetries.
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