Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly model conditions using latent condition variables and a shared GP | GP-ind · Synthetic data without missing data | performance: 0.24\pm0.02 -> 0.20\pm0.02 | Helped |
| Use latent condition variables and a shared GP instead of LMC | LMC · Synthetic data without missing data | performance: 0.28\pm0.11 -> 0.20\pm0.02 | Helped |
| Jointly model conditions using latent condition variables and a shared GP | GP-ind · Synthetic data with missing data | performance: 0.43\pm0.06 -> 0.30\pm0.04 | Helped |
| Use latent condition variables and a shared GP instead of LMC | LMC · Synthetic data with missing data | performance: 0.47\pm0.09 -> 0.30\pm0.04 | Helped |
| Jointly model conditions using latent condition variables and a shared GP | GP-WO · Servo Data | RMSE: 1.03\pm0.20 -> 0.52\pm0.16 | Helped |
| Jointly model conditions using latent condition variables and a shared GP | GP-ind · Servo Data | RMSE: 1.30\pm0.31 -> 0.52\pm0.16 | Helped |
| Use latent condition variables instead of one-hot condition encoding | GP-OH · Servo Data | RMSE: 0.73\pm0.26 -> 0.52\pm0.16 | Helped |
| Use latent condition variables and a shared GP instead of LMC | LMC · Servo Data | RMSE: 0.69\pm0.35 -> 0.52\pm0.16 | Helped |
| Jointly model conditions using latent condition variables and a shared GP | GP-ind · Sensor imputation | RMSE: 0.85\pm0.09 -> 0.45\pm0.02 | Helped |
| Use latent condition variables and a shared GP instead of LMC | LMC · Sensor imputation | RMSE: 0.59\pm0.21 -> 0.45\pm0.02 | Helped |
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