A new research paper explores the sample complexity of scientific discovery through the lens of PAC learning, focusing on compositional function trees. The study proves that the generalization quantity, Rademacher complexity, is controlled by the depth of the tree and the Lipschitz constants of its operators, rather than growing exponentially. The authors developed a codebase to train differentiable operator trees and empirically demonstrated that the generalization gap correlates with their predicted complexity. AI
IMPACT Provides theoretical grounding for symbolic regression and compositional function learning, potentially improving AI's ability to perform scientific discovery.
RANK_REASON The cluster contains an academic paper detailing theoretical research on machine learning.
- arXiv
- Compositional Function Trees
- Lipschitz constants
- PAC learning
- Rademacher complexity
- Shuayp Talha Kocabay
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →
