Research map / Probabilistic and scientific modeling / Bayesian optimization
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SOT instead of Hellinger for meta-regression | Kernel log-evidence prediction · Airfoil | RMSE: 0.1773 -> 0.0936 | Helped |
| Use SOT instead of Hellinger for meta-regression | Kernel log-evidence prediction · Airline | RMSE: 0.3569 -> 0.3464 | Helped |
| Use SOT instead of Hellinger for meta-regression | Kernel log-evidence prediction · LGBB | RMSE: 0.3772 -> 0.2783 | Helped |
| Use SOT instead of Hellinger for meta-regression | Kernel log-evidence prediction · Powerplant | RMSE: 0.1912 -> 0.0137 | Helped |
| Use SOT instead of kNN for meta-regression | Kernel log-evidence prediction · Concrete | RMSE: 0.1806 -> 0.0451 | Helped |
| Use SOT kernel search instead of Tree-GEP | Gaussian-process kernel selection · Airline | NLL: -0.5594 -> -0.7015 | Helped |
| Use SOT kernel search instead of Tree-GEP | Gaussian-process kernel selection · LGBB | NLL: -1.0701 -> -0.9325 | Hurt |
| Use SOT kernel search instead of Tree-GEP | Gaussian-process kernel selection · Powerplant | NLL: -0.0241 -> -0.0661 | Helped |
| Use SOT kernel search instead of Greedy | Gaussian-process kernel selection · Airfoil | NLL: 0.0837 -> 0.1006 | Hurt |
| Use SOT kernel search instead of Tree-GEP | Gaussian-process kernel selection · Concrete | NLL: 0.2812 -> 0.2872 | Hurt |
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