Superconductivity
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Data Imputation | superconductivity | Wasserstein Distance0.3109 | 216 | |
| Imputation | Superconductivity (MAR) | RMSE0.0959 | 72 | |
| Imputation | Superconductivity (MCAR) | RMSE0.0908 | 72 | |
| Imputation | Superconductivity MNAR | RMSE0.2724 | 70 | |
| OOD detection | Superconductivity (test) | AU-ROC (AU)0.887 | 24 | |
| Linear Regression | Superconductivity 20 MCAR injected (test) | Time (s)0.0692 | 19 | |
| Linear Regression | Superconductivity 60 | Time (s)6.5 | 16 | |
| Linear Regression | Superconductivity 40 | Time (s)14.08 | 16 | |
| Linear Regression | Superconductivity 20 | Time (s)11.76 | 16 | |
| Linear Regression | Superconductivity (60% MNAR) | Time (s)0.0091 | 16 | |
| Linear Regression | Superconductivity 40% MNAR | Time (s)9.86 | 16 | |
| Linear Regression | Superconductivity 20% MNAR | Time (s)11.76 | 16 | |
| Multimodal regression | Superconductivity (test) | RMSE10.37 | 13 | |
| Downstream prediction | Superconductivity MNAR (test) | RMSE0.2803 | 8 | |
| Downstream prediction | Superconductivity MAR (test) | RMSE0.2703 | 8 | |
| Downstream prediction | Superconductivity MCAR (test) | RMSE0.2768 | 8 | |
| Regression | superconductivity outliers (test) | Mean Outlier Coverage92 | 7 | |
| Regression | superconductivity n=21263 (test) | ILR1.09 | 7 | |
| Regression | Superconductivity UCI (test) | RMSE11.034 | 6 | |
| Regression | Superconductivity (SC) UCI (test) | PICP98.3 | 6 | |
| Regression | superconductivity (test) | Conditional Large-Loss Rate18 | 6 | |
| Selective Regression | superconductivity (test) | Conditional Large-Loss Rate17.9 | 6 | |
| Synthetic Data Generation | Superconductivity | Average Runtime (s)2.5845 | 5 | |
| Linear Regression | Superconductivity 60 MCAR injected (test) | Time (s)1.85 | 5 | |
| Linear Regression | Superconductivity 40 MCAR injected (test) | Time (s)2.05 | 5 |
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