Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Scalable Exact Inference in Multi-Output Gaussian Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use OILMM instead of IGP | Temperature extrapolation · Temperature extrapolation, m=5 | PPLP: -1.923 -> -1.457 | Helped |
| Increase OILMM latent-process count from m=1 to m=247 (ablation) | OILMM · Temperature extrapolation | RMSE: 2.151 -> 1.991 | Helped |
| Increase OILMM latent-process count from m=1 to m=247 (ablation) | OILMM · Temperature extrapolation | PPLP: -1.976 -> -0.525 | Helped |
| Use OILMM with m=200 instead of IGP | Temperature extrapolation · Temperature extrapolation | RMSE: 1.993 -> 1.992 | No clear effect |
| Use OILMM instead of unrestricted ILMM | Exchange rates · Exchange rates | SMSE: 0.19 -> 0.19 | No clear effect |
| Use OILMM instead of unrestricted ILMM | Exchange rates · Exchange rates | PPLP: 3.39 -> 3.39 | No clear effect |
| Use OILMM instead of unrestricted ILMM | EEG · EEG | SMSE: 0.49 -> 0.49 | No clear effect |
| Use OILMM instead of unrestricted ILMM | EEG · EEG | PPLP: -2.11 -> -2.11 | No clear effect |
| Apply OILMM to exact inference and learning instead of ILMM | Multi-output GP inference · Computational scaling in latent-process count m | runtime complexity: O(n^3 m^3) -> O(n^3 m) | Helped |
| Apply OILMM to exact inference and learning instead of ILMM | Multi-output GP inference · Computational scaling in latent-process count m | memory complexity: O(n^2 m^2) -> O(n^2 m) | Helped |
| Add variational inducing-point scaling to OILMM | OILMM · Large-scale climate model calibration | runtime complexity: O(n^3 m) -> O(n m r^2) | Helped |
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