New analysis shows linear ensemble sampling matches Thompson sampling

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]

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• arXiv
• David Janz
• Ensemble Sampling
• Gaussian perturbations
• Stochastic Linear Bandits
• Thompson sampling

• paper
• other

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