Bewaard in:
| Hoofdauteurs: | , , |
|---|---|
| Formaat: | Preprint |
| Gepubliceerd in: |
2026
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| Onderwerpen: | |
| Online toegang: | https://arxiv.org/abs/2602.08026 |
| Tags: |
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Inhoudsopgave:
- We analyse linear ensemble sampling (ES) with standard Gaussian perturbations in stochastic linear bandits. We show that for ensemble size $m=Θ(d\log n)$, ES attains $\tilde O(d^{3/2}\sqrt n)$ high-probability regret, closing the gap to the Thompson sampling benchmark while keeping computation comparable. The proof brings a new perspective on randomized exploration in linear bandits by reducing the analysis to a time-uniform exceedance problem for $m$ independent Brownian motions. Intriguingly, this continuous-time lens is not forced; it appears natural--and perhaps necessary: the discrete-time problem seems to be asking for a continuous-time solution, and we know of no other way to obtain a sharp ES bound.