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Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use partial Cholesky preconditioning | GP regression with Matérn(3/2) · Kin40k | Runtime (s): 186.545455 -> 44.636364 | Helped |
| Use partial Cholesky preconditioning | GP regression with Matérn(3/2) · Protein | Runtime (s): 892.636364 -> 42.545455 | Helped |
| Use partial Cholesky preconditioning | GP regression with Matérn(3/2) · KEGGdir | Runtime (s): 1450.272727 -> 173.727273 | Helped |
| Use partial Cholesky preconditioning | GP regression with Matérn(3/2) · 3DRoad | Runtime (s): 82200.000000 -> 7306.000000 | Helped |
| Use partial Cholesky preconditioning | GP regression with Matérn(3/2) · Bike | Runtime (s): 30.636364 -> 37.090909 | Hurt |
| Use partial Cholesky preconditioning | GP regression with Matérn(3/2) · Elevators | -L_test: 0.402140 -> 0.402184 | No clear effect |
| Use partial Cholesky preconditioning | GP regression with Matérn(3/2) · UCI datasets | training loss (-L_train) | Helped |
| Use partial Cholesky preconditioning | GP regression with Matérn(3/2) · UCI datasets | generalization error | No clear effect |
| Use preconditioning in stochastic trace estimation | GP marginal likelihood and its derivatives · Synthetic data | relative error | Helped |
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