German Credit
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Credit Risk Prediction | German Credit (test) | Clean Accuracy69.7 | 31 | |
| Coreset Construction | German Credit | Wasserstein Distance0.23 | 28 | |
| DP Synthetic Data Generation | German Credit | TSTR78.1 | 24 | |
| Classification | German Credit UCIrvine (5-fold cross-val) | Macro F10.796 | 17 | |
| Classification | German Credit (test) | Accuracy77.66 | 16 | |
| Fair Classification | German Credit (test) | Equal Opportunity Difference5.15 | 15 | |
| Classification | German Credit | AU-ARC0.8388 | 10 | |
| Classification | German Credit UCIrvine | Macro F179.3 | 9 | |
| Classification | German Credit | Classification Error33 | 8 | |
| Counterfactual Explanation Generation | German Credit Protocol A B=4 | Sparsity88.23 | 6 | |
| Selective Classification | German Credit | AU-ARC83.8 | 5 | |
| Tabular Classification | GE (German Credit) (test) | MLE Loss0.406 | 5 | |
| Node Classification | German Credit (test) | AUROC59.88 | 4 | |
| Recourse Cost Evaluation | German Credit | Protected Recourse Cost5.65 | 4 | |
| Reliability Assessment | German Cr. | AU-ARC (uH)0.8338 | 1 | |
| ESS estimation | German Credit | Metric- | 0 |
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