Research map / Label-efficient and robust learning / Semi-supervised learning
Learning Safe Prediction for Semi-Supervised Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace uniform voting of base regressors with SAFER projection weights (ablation) | semi-supervised regressor ensemble · Average MSE, 5 labeled | MSE: .033 -> .032 | Helped |
| replace uniform voting with SAFER projection weights (ablation) | semi-supervised regressor ensemble · Average MSE, 10 labeled | MSE: .026 -> .027 | No clear effect |
| replace uniform voting with SAFER on bodyfat (5 labeled) (ablation) | semi-supervised regressor ensemble · bodyfat, 5 labeled | MSE: .031 -> .025 | Helped |
| replace uniform voting with SAFER on space ga (5 labeled) (ablation) | semi-supervised regressor ensemble · space ga, 5 labeled | MSE: .008 -> .004 | Helped |
| use unlabeled data via SAFER safe combination instead of supervised 1NN | 1NN · Average MSE, 5 labeled | MSE: .039 -> .032 | Helped |
| use unlabeled data via SAFER instead of supervised 1NN | 1NN · Average MSE, 10 labeled | MSE: .034 -> .027 | Helped |
| use Self-LS semi-supervised extension of least squares | 1NN · Average MSE, 5 labeled | MSE: .039 -> .044 | Hurt |
| evaluate SAFER with MAE and MEE instead of MSE (ablation) | SAFER · 10 datasets | MAE, MEE | No clear effect |
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