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New analysis shows linear ensemble sampling matches Thompson sampling

By PulseAugur Editorial · [1 sources] ·

Researchers have published a new analysis of linear ensemble sampling (ES) in stochastic linear bandits, demonstrating its effectiveness with standard Gaussian perturbations. The study shows that ES can achieve a regret of \tilde O(d^{3/2}\sqrt n) with an ensemble size of m=\Theta(d\log n), matching the performance of Thompson sampling while maintaining comparable computational costs. The novel proof technique involves reducing the analysis to a time-uniform exceedance problem for independent Brownian motions, offering a new perspective on randomized exploration in linear bandits. AI

RANK_REASON Academic paper published on arXiv detailing a new analysis of an existing algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New analysis shows linear ensemble sampling matches Thompson sampling

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Academic paper published on arXiv detailing a new analysis of an existing algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · David Janz, Arya Akhavan, Csaba Szepesv\'ari ·

    Sharp analysis of linear ensemble sampling

    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, closi…

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