Research map / Learning theory and algorithms / Prediction and decision losses
Online Symbolic Regression with Informative Query
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use QUOSR-generated queries instead of uniform sampling | Nesymres · Symbolic regression test set | R^2: 0.75 -> 0.86 | Helped |
| Use query-by-distribution instead of query-by-set (ablation) | QUOSR with SymbolicGPT · SymbolicGPT test set | R^2: 0.5388 -> 0.6177 | Helped |
| Use attention intersection instead of mean intersection (ablation) | QUOSR with SymbolicGPT · SymbolicGPT test set | R^2: 0.5370 -> 0.6177 | Helped |
| Use attention intersection instead of max intersection (ablation) | QUOSR with SymbolicGPT · SymbolicGPT test set | R^2: 0.5774 -> 0.6177 | Helped |
| Use KL similarity instead of cosine similarity (ablation) | QUOSR with SymbolicGPT · SymbolicGPT test set | R^2: 0.5380 -> 0.6177 | Helped |
| Represent the physical system with data points instead of symbolic forms (ablation) | QUOSR with SymbolicGPT · SymbolicGPT test set | R^2: 0.5721 -> 0.6177 | Helped |
| Use query-by-distribution instead of query-by-set, with expression representation (ablation) | QUOSR with SymbolicGPT · SymbolicGPT test set | R^2: 0.5290 -> 0.6177 | Helped |
Closest papers by meaning
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