Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Near-Optimal Active Learning of Multi-Output Gaussian Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use m-Greedy to select locations and measurement types jointly | CMOGP active learning · Jura | RMSE | Helped |
| Use m-Greedy instead of m-Var for joint location and measurement-type selection | CMOGP active learning · Jura | RMSE | Helped |
| Use m-Greedy to select locations and measurement types jointly | CMOGP active learning · Gilgai | RMSE | Helped |
| Use m-Greedy to select locations and measurement types jointly | CMOGP active learning · IEQ | RMSE | Helped |
| Use m-Greedy to select auxiliary temperature measurements when predicting light | CMOGP active learning · IEQ | RMSE | Helped |
| Increase the number of inducing locations | m-Greedy and m-Var · Jura | RMSE | Helped |
| Use higher-quality Ni measurements as the target instead of lg-Cd | m-Greedy · Jura | RMSE | Helped |
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