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Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach

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arXiv:2606. 20022v1 Announce Type: new Abstract: This paper considers stochastic linear contextual bandits (SLCB) with bounded reward noise.

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Authors: Haonan Xu, Yingying Li
Released: 2026-06-19 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach 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, text, under, line, does. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.20022

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