Tabular Regression on Carbon UCI 10% outlier contamination
0.013RMSEGrad. (Primal)
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Grad. (Primal)Outlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=10, Model=Heteroscedastic SBL2026.05 | 0.013 | 4.2 | 89.6 | |
| Grad. (Dual)Outlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=10, Model=Heteroscedastic SBL2026.05 | 0.013 | 3.7 | 89.6 | |
| ℓ2-IRLSOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=10, Model=Heteroscedastic SBL2026.05 | 0.014 | 22.7 | 91.5 | |
| HuberOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=102026.05 | 0.015 | 100 | 90.1 | |
| EMOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=10, Model=Heteroscedastic SBL2026.05 | 0.015 | 15.3 | 92 | |
| MacKayOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=10, Model=Heteroscedastic SBL2026.05 | 0.016 | 2.7 | 90.8 | |
| ℓ1-IRLSOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=10, Model=Heteroscedastic SBL2026.05 | 0.02 | 1.9 | 93.1 | |
| Student-tOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=102026.05 | 0.028 | 100 | 90.2 | |
| RidgeOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=102026.05 | 0.053 | 92.6 | 100 | |
| GPOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=102026.05 | 0.053 | 16.9 | 100 | |
| OLSOutlier Contamination=10%, Feature Mapping=Random Fourier Features (RFF), Dimensions (d)=256, Trials=102026.05 | 0.118 | 100 | 100 |