Peak prediction (Task 1) on CMI-PB (test)
0.816AUROCLogistic regression
Evaluation results
| Method | Links | ||
|---|---|---|---|
| Logistic regressionInput features=TabPFN-v2 embeddings (concatenation), Embedding dimensions=6,1442026.05 | 0.816 | 0.659 | |
| multi-task contrastive multimodal fusion architectureInput features=TabPFN-v2 embeddings (concatenation), Method Label=Preferred (ours)2026.05 | 0.797 | 0.621 | |
| TabMLPInput features=TabPFN-v2 embeddings (concatenation), Embedding dimensions=6,1442026.05 | 0.785 | 0.623 | |
| XGBoostInput features=Raw features (mean-imputed concatenation), Imputation method=per-feature training-set mean imputation2026.05 | 0.781 | 0.599 | |
| Logistic regressionInput features=Raw features (mean-imputed concatenation), Imputation method=per-feature training-set mean imputation2026.05 | 0.699 | 0.495 | |
| TabMLPInput features=Raw features (mean-imputed concatenation), Imputation method=per-feature training-set mean imputation2026.05 | 0.594 | 0.384 | |
| XGBoostInput features=TabPFN-v2 embeddings (concatenation), Embedding dimensions=6,1442026.05 | 0.5 | 0.5 |