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Adaptive data selection improves wearable prediction under low baseline performance

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arXiv:2606. 00141v1 Announce Type: new Abstract: Adaptive sensing strategies that selectively sample data are increasingly used in wearable health systems to improve prediction performance under limited data budgets, yet their benefits across individuals remain poorly understood.

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Authors: Ali Kargarandehkordi
Released: 2026-06-02 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Adaptive data selection improves wearable prediction under low baseline performance overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, under, eval, line, rate, test, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.00141

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