Upper Bounds on the Generalization Error of Deep Learning Models via Local Robustness and Stability
arXiv:2606.16883v1 Announce Type: cross Abstract: Generalization is a critical property of data-driven models, particularly deep learning models deployed in safety-critical applications. Robustness-based generalization bounds have gained attention as a principled way to link robustness properties to generalization performance, often in a data-dependent manner. However, most existing bounds suffer from vacuousness in practical settings, yielding loose upper bounds that greatly exceed the actual error rates and limiting their usefulness for real-world evaluation. While this issue is often attrib
This research addresses a long-standing challenge in deep learning generalization, driven by the increasing deployment of AI in sensitive applications and the need for more reliable performance guarantees.
Improving the accuracy and reliability of generalization error bounds is crucial for developing trustworthy AI, especially as models are integrated into safety-critical systems.
More effective methods for quantifying generalization error could enable broader adoption of deep learning in regulated industries and foster greater confidence in AI system deployment.
- · AI safety researchers
- · Deep learning framework developers
- · Regulated industries deploying AI
- · Developers of 'black box' AI models
- · Sectors reliant on unverified AI performance
New theoretical upper bounds could inform improved model architectures and training methodologies that inherently generalize better.
Enhanced trustworthiness metrics for AI could become a competitive advantage, leading to industry standards for 'guaranteed generalization'.
Increased regulatory scrutiny on AI generalization could drive a demand for provably robust models, reshaping the AI development lifecycle significantly.
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Read at arXiv cs.AI