Improving Neural Symbolic Regression with a Diverse Prior and Post-training Alignment
Symbolic regression is the problem of finding an expression that fits a dataset.
ProofPaper ↗
Key points
- Neural symbolic regression can amortize that search by training a model once on synthetic data and then predicting expressions for a new dataset.
- We argue that the deficit lies in the training rather than in the paradigm, and present RASR (Reinforced Amortized Symbolic Regression), a method with two key changes to the training recipe.
- First, we design a synthetic prior that narrows the gap with real-world data: inputs come from the prior of a tabular foundation model; constants are sampled bottom-up, conditioned on the data; targets carry heteroscedastic and structured noise; and everything is standardized.
- Supervised pre-training maximizes the token-level likelihood of a reference expression.
Sources (1)
- [1]Improving Neural Symbolic Regression with a Diverse Prior and Post-training AlignmentarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 08:57 AM
Symbolic regression is the problem of finding an expression that fits a dataset.
Neural symbolic regression can amortize that search by training a model once on synthetic data and then predicting expressions for a new dataset.
Extractive summary: sentences quoted from the sources.
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