Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sVBSSGP instead of the strongest reported baseline, PIC+ | Sparse spectrum GP regression on AIMPEAK · AIMPEAK | RMSE: 6.15 -> 4.73 | Helped |
| Use sVBSSGP instead of the strongest reported baseline, PIC+ | Sparse spectrum GP regression on AIRLINE · AIRLINE | RMSE: 33.40 -> 22.18 | Helped |
| Set the test-conditional mixing parameter to gamma = 0 rather than higher tested values (ablation) | sVBSSGP · AIMPEAK | RMSE | Helped |
| Use more posterior samples for prediction rather than r = 1 (ablation) | sVBSSGP · BLOG | RMSE | Helped |
| Use r = 1 posterior sample instead of more samples (ablation) | sVBSSGP · BLOG | RMSE | Hurt |
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