arXiv:2604.17402v2 Announce Type: replace Abstract: Symbolic regression (SR) with genetic programming (GP) aims to discover interpretable mathematical expressions directly from data. Despite its strong empirical success, the theoretical understanding of why GP-based SR generalizes beyond the training data remains limited. In this work, we provide a learning-theoretic analysis of SR models represented as expression trees. We derive a generalization bound for GP-style SR under constraints on tree size, depth, and learnable constants. Our result decomposes the generalization gap into two interpre
This work is a theoretical advancement in AI, specifically in understanding the generalization capabilities of an established technique, indicating a push towards more robust foundational AI research.
Improved theoretical understanding of AI models like symbolic regression can lead to more reliable, interpretable, and deployable AI systems, enhancing trustworthiness and applicability in critical domains.
The ability to derive generalization bounds for symbolic regression provides a stronger theoretical underpinning for its use, potentially accelerating its adoption in areas requiring proven reliability.
- · AI researchers
- · Machine learning developers
- · Sectors requiring interpretable AI
- · Opponents of 'black box' AI
- · Purely empirical AI development approaches
This research provides a theoretical framework to predict how well symbolic regression models will perform on unseen data.
Better predictability could lead to more widespread adoption of symbolic regression in scientific discovery and engineering, where interpretability is paramount.
The enhanced confidence in GP-based SR might indirectly influence the development of more advanced, provably robust AI agents capable of complex reasoning.
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