Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Dirichlet-transformed labels with heteroskedastic GP regression instead of variational GP classification | Gaussian-process classification · UCI classification datasets | training time | Helped |
| Use Dirichlet-transformed labels with heteroskedastic GP regression instead of direct GP regression on labels | Gaussian-process classification · UCI classification datasets | calibration | Helped |
| Apply Platt scaling after GP regression on labels | GPR · Binary classification datasets | calibration | Helped |
| Increase the number of inducing points (ablation) | GPD · Letter, MiniBoo, and Mocap | error rate | Helped |
| Vary the Dirichlet prior parameter αε (ablation) | GPD · HTRU2, MAGIC, Letter, and Drive | MNLL | Mixed |
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