Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| BBMM inference instead of Cholesky | Exact GP · UCI datasets | speed: up to 20× faster | Helped |
| BBMM inference instead of Cholesky | Exact GP and SGPR · UCI datasets | test MAE | No clear effect |
| BBMM instead of Dong et al. sequential inference | SKI with deep kernels · large UCI datasets | speed: up to 15× | Helped |
| add rank-5 pivoted Cholesky preconditioner (ablation) | BBMM conjugate gradients · Protein, KEGG | solve error / test MAE per wall-clock | Helped |
| increase preconditioner rank (0,2,5,9) (ablation) | BBMM conjugate gradients · Protein, KEGG | CG iterations to converge | Helped |
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