arXiv:2602.08026v2 Announce Type: replace Abstract: We analyse linear ensemble sampling (ES) with standard Gaussian perturbations in stochastic linear bandits. We show that for ensemble size $m=\Theta(d\log n)$, 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. This continuous-time lens appears particularly natural he
This paper presents a new theoretical advancement in the analysis of linear ensemble sampling, connecting it to established benchmarks like Thompson sampling in stochastic linear bandits.
Improved theoretical understanding of exploration-exploitation trade-offs in AI algorithms can lead to more efficient and robust machine learning systems, impacting various applications.
The analysis offers a tighter theoretical bound for ensemble sampling, potentially making it a more attractive option for efficient exploration in linear bandit problems.
- · AI researchers
- · Machine learning practitioners
- · SaaS companies utilizing bandit algorithms
The improved theoretical understanding of ensemble sampling could lead to its increased adoption in practical AI systems.
More efficient bandit algorithms can optimize decision-making processes in areas like online advertising, recommendation systems, and clinical trials.
Widespread adoption could subtly enhance the performance and resource efficiency of AI-driven platforms, contributing to broader AI development trends.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG