New Thompson Sampling variant explains variance inflation in bandit problems

Researchers have introduced \"$\alpha$-TS\", a variant of the Thompson Sampling algorithm designed for generalized linear bandit problems. This new approach formalizes the concept of variance inflation, which is necessary for achieving near-optimal regret guarantees in existing analyses. The study outlines general conditions under which $\alpha$-TS can be analyzed without needing tractable posterior approximations, a departure from prior work. The findings establish a regret bound of $O(d^{3/2}\sqrt{T}\log T)$ for specific reward distributions and provide a lower bound that explains the origin of the $d^{3/2}$ factor in the upper bound. AI

IMPACT Introduces a novel algorithmic approach for bandit problems, potentially improving decision-making in AI systems.

RANK_REASON Academic paper detailing a new algorithm and its theoretical analysis. [lever_c_demoted from research: ic=1 ai=1.0]

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• $\alpha$-TS
• arXiv
• Bayesian statistics
• Generalized Linear Bandit
• Thompson sampling

• paper
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