Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite $L_p$ Moments
arXiv:2606.06855v1 Announce Type: cross Abstract: While algorithmic stability is a central tool for understanding generalization of learning algorithms, existing high-probability guarantees typically rely on uniform boundedness or sub-Gaussian/sub-Weibull tail assumptions, which can be overly restrictive for modern settings with heavy-tailed or unbounded losses. We develop a stability-based framework that requires only a finite $L_p$ moment condition. Our first contribution is sharp concentration inequalities for functions of independent random variables under $L_p$ constraints, extending McDi
The proliferation of advanced AI models and complex real-world data necessitates more robust theoretical underpinnings for generalization, especially regarding heavy-tailed distributions and unbounded losses.
This research provides sharper and more generalizable bounds for AI model stability, which is critical for developing more reliable, safe, and deployable AI systems across various applications, including agentic systems.
The ability to accurately predict and guarantee the generalization performance of AI models is enhanced, moving beyond restrictive assumptions of uniform boundedness, thereby expanding the scope of deployable AI.
- · AI researchers and developers
- · Companies deploying AI in complex, real-world environments
- · Sectors reliant on robust AI (e.g., finance, healthcare, autonomous systems)
- · Organizations relying on less robust, older generalization theories
- · Approaches limited by sub-Gaussian assumptions
Improved theoretical understanding of AI generalization leads to more stable and trustworthy AI models.
Increased confidence in AI model performance accelerates development and deployment of agentic systems and other advanced AI applications.
Broader adoption of AI in high-stakes environments due to enhanced reliability and explainability enabled by rigorous generalization bounds.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG