arXiv:2606.20022v1 Announce Type: cross Abstract: This paper considers stochastic linear contextual bandits (SLCB) with bounded reward noise. Existing works typically assume sub-Gaussian reward noise and bounded expected rewards, under which the optimal regret bound scales as $\tilde{O}(\sqrt{T})$ in terms of horizon $T$. However, in many applications, realized/observed rewards are also naturally bounded, implying bounded reward noise. Bounded noise is more informative than the sub-Gaussian condition but has not been leveraged explicitly in the SLCB literature. In this paper, we propose a nove
This is a new academic paper published on arXiv, a standard venue for presenting early-stage research in AI.
This paper presents a technical refinement in the field of contextual bandits, which is a niche area of machine learning theory that does not directly impact strategic considerations.
This research potentially improves the theoretical understanding and performance bounds for a specific type of machine learning algorithm under certain conditions, but it does not alter current AI development or application trajectories.
Further academic research might build upon these theoretical improvements in contextual bandits.
Improved theoretical understanding could, in the very long term, subtly influence the design of some reinforcement learning systems.
It is highly unlikely to have any discernible impact on broader technological or economic trends.
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Read at arXiv cs.LG