CA
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Spatio-temporal forecasting | CA | Relative MAE0.12 | 32 | |
| Next POI Prediction | CA | ACC@120.95 | 18 | |
| Mobility Prediction | CA Foursquare Gowalla (test) | M@122.25 | 15 | |
| Next-location prediction | CA (test) | Top-1 Accuracy17.3 | 14 | |
| Energy usage prediction | CA California | MAE0.327 | 14 | |
| Combinatorial Auction | CA Large-scale | Objective Value13,592.81 | 12 | |
| Next POI recommendation | CA | ND@10.1595 | 12 | |
| Federated Clustering | CA | NMI0.39 | 9 | |
| Global Clustering | CA | Purity0.436 | 9 | |
| Clustering | CA | Purity54.2 | 9 | |
| Global Clustering | CA | SC0.139 | 9 | |
| Tabular Data Clustering | CA | ARI0.1862 | 8 | |
| Solving MILP instances | CA (test) | Avg Time (s)0.02 | 8 | |
| Traffic Forecasting | CA | MAE (Horizon 3)14.69 | 7 | |
| 3D Object Detection | CA-1M | AP@0.25 IoU40.9 | 7 | |
| Structural Distributional Similarity | CA | SDS Score99.7 | 7 | |
| Combinatorial Auction | CA Medium Scale | Gap Improvement100 | 6 | |
| Tabular Classification | CA | Macro F197.4 | 6 | |
| Mixed Integer Linear Programming Solving | CA | Objective Value7,453.42 | 5 | |
| Structured Sentiment Analysis | CA v1.0 (test) | Holder Span F160.8 | 5 | |
| Tabular Classification | CA (test) | AUROC96.7 | 4 | |
| Synthetic Tabular Data Generation | CA | DCR11.7 | 4 | |
| Preference Prediction | CA | Number of Significant Features (S)9 | 2 | |
| Time Series Forecasting | CA (Average) | MAE17.5 | 2 | |
| Tabular Data Generation | CA | MLE0.838 | 2 |
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