Research map / Label-efficient and robust learning / Domain adaptation and generalization
Source-Target Similarity Modelings for Multi-Source Transfer Gaussian Process Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Joint learning of model importance and similarity coefficients (TCMSStack) vs. pretrained GP-TCSS stacking (TCSSStack) (ablation) | stacking of transfer covariance GP base models · Amazon and UJIIndoorLoc real-world datasets | RMSE | Helped |
| Stacking with TCMSStack instead of GP-TCMS (single shared similarity coefficient) | multi-source transfer GP · Synthetic dataset, 2/5/10/15 sources | RMSE | Helped |
| Stacking with TCMSStack instead of GP-TCMS (single shared similarity coefficient) | multi-source transfer GP · Amazon and UJIIndoorLoc real-world datasets | RMSE | Helped |
| Replace TCMSStack with TrAdaBoost.R2 boosting | multi-source transfer regression · Amazon and UJIIndoorLoc real-world datasets | RMSE | Hurt |
| Learn similarity coefficients as model importance (λ-Stacking) instead of separate weights vs. TCSSStack (ablation) | TCSSStack · Amazon and UJIIndoorLoc real-world datasets | RMSE | No clear effect |
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