coreset constructions
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Data-efficient flood depth prediction through domain-aware coreset selection and tabular foundation models
Announce Type: new Abstract: Near-real-time flood depth prediction demands surrogate models that are accurate, fast, and transferable across watersheds. Supervised surrogates can match physics-based simulators in accuracy but need millions of training rows per watershed and cannot extrapolate beyond their original mesh. We propose a domain-aware coreset construction pipeline that conditions a tabular foundation model at inference time.
Towards Tight Bounds for Streaming Attention
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