Research map / Learning theory and algorithms / Combinatorial and search optimization
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Pareto-frontier pruning, hypothesis-test rejection and generalized modularity discovery instead of earlier AI Feynman | AI Feynman · Feynman SR database harder 20 | fraction solved: 85% -> 90% | Helped |
| use generalized modularity discovery and Pareto pruning instead of earlier AI Feynman | AI Feynman · 12 modular equations | fraction solved: 42% -> 100% | Helped |
| add normalizing-flow density estimation for symbolic regression from samples | AI Feynman · 10 probability distributions | fraction solved: 70% -> 80% | Helped |
| new method vs previous on basic 100 problems | AI Feynman · Feynman SR database basic 100 | fraction solved: 100% -> 100% | No clear effect |
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- Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$ (ICML 2025 · similarity 0.87)
- Symbolic Regression with a Learned Concept Library (NeurIPS 2024 · similarity 0.87)
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