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New Thompson Sampling variant \"alpha-TS\" offers generalized regret analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a generalized regret analysis for Thompson sampling, a popular algorithm for solving stochastic multi-armed bandit problems. This new approach, termed \"alpha-TS,\" utilizes a fractional posterior distribution instead of the standard one by tempering the likelihood with a factor \"alpha.\" The analysis yields both instance-dependent and instance-independent frequentist regret bounds under mild conditions on prior and reward distributions, applicable to sub-Gaussian and exponential family models. The derived bounds match existing improved UCB bounds and do not require specific structural properties like closed-form or conjugate priors. AI

IMPACT This theoretical advancement in regret analysis could lead to more efficient multi-armed bandit algorithms, impacting areas like online learning and recommendation systems.

RANK_REASON The cluster contains an academic paper detailing a new theoretical analysis of an existing algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New Thompson Sampling variant \"alpha-TS\" offers generalized regret analysis

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The cluster contains an academic paper detailing a new theoretical analysis of an existing algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya, Bani K. Mallick ·

    Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors

    arXiv:2309.06349v2 Announce Type: replace Abstract: Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of TS, named $\alpha$-TS, where we use a fractional or $\alpha$-posterior ($\alpha\…

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