CRISP: Scalable Importance-Stratified Coresets for Imbalanced Tabular Learning
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 removed. We present CRISP (Coreset Reduction via Importance-Stratified Pruning), a l…
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- 2026-09-24 04:00 · arXiv cs.LG
CRISP: Scalable Importance-Stratified Coresets for Imbalanced Tabular Learning