CRISP: scalable importance-stratified coresets for imbalanced tabular learning
Read the original at arxiv.org→arXiv:2609.26962v1 Announce Type: new Abstract: Large imbalanced tabular datasets make repeated gradient-boosted tree training expensive. Existing coreset methods often lose accuracy when most majority examples are...
Original headline: "CRISP: Scalable Importance-Stratified Coresets for Imbalanced Tabular Learning"
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- Sep 24, 04:00 UTC arXiv cs.LG lead source CRISP: Scalable Importance-Stratified Coresets for Imbalanced Tabular Learning