Research map / Label-efficient and robust learning / Semi-supervised learning
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add unlabeled-data predictive-variance minimization to supervised DKL | DKL · UCI regression (8 datasets), n=100 | median percent RMSE reduction vs. DKL: 4.20 | Helped |
| Add unlabeled-data predictive-variance minimization to supervised DKL | DKL · UCI regression (8 datasets), n=300 | median percent RMSE reduction vs. DKL: 5.81 | Helped |
| Add a separate location kernel to the image-feature kernel (ablation) | SSDKL · Poverty prediction (5 countries), n=300 | median percent RMSE reduction vs. ridge regression: 15.5 -> 17.9 | Helped |
| Use a polynomial kernel instead of the RBF kernel (ablation) | SSDKL · UCI regression | test RMSE | Hurt |
| Vary the variance-regularization weight α from 0.1 to 10.0 (ablation) | SSDKL · Poverty prediction, n=300 | test performance | No clear effect |
| Concatenate location coordinates with image features rather than using a separate location kernel (ablation) | SSDKL · Poverty prediction | test RMSE | No clear effect |
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