Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Pólya-Gamma augmentation with closed-form stochastic natural-gradient updates instead of SVGPC | GP classification · Higgs | Time (seconds): 294 ± 54 -> 23 ± 0.88 | Helped |
| Use Pólya-Gamma augmentation with closed-form stochastic natural-gradient updates instead of EPGPC | GP classification · SUSY | Time (seconds): 10,000 -> 14 ± 0.29 | Helped |
| Use X-GPC rather than EPGPC | GP classification · IJCNN | Error: 0.02 ± 0.01 -> 0.03 ± 0.01 | Hurt |
| Use X-GPC rather than EPGPC | GP classification · wXa | Time (seconds): 1.4 ± 0.10 -> 66 ± 16 | Hurt |
| Increase the number of inducing points (ablation) | X-GPC, SVGPC, and EPGPC · Shuttle | Prediction error | Helped |
| Use fewer than 128 inducing points (ablation) | SVGPC · Shuttle | Prediction error | Mixed |
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