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Thompson Sampling Proven 2-Competitive for Mistakes in Bayesian Bandit Models

By PulseAugur Editorial · [2 sources] ·

A new paper published on arXiv details a theoretical advancement in Bayesian bandit models, proving that Thompson sampling is 2-competitive in terms of mistakes. This means Thompson sampling makes at most twice the expected number of errors compared to any other policy. The analysis holds for independent latent arm processes where arms evolve only when played, confirming a 2014 conjecture by Guha and Munagala for stochastic bandits. The result is applicable under various weighting schemes, including fixed horizons and geometric discounting. AI

IMPACT Provides theoretical guarantees for a common algorithm in reinforcement learning and decision-making under uncertainty.

RANK_REASON Academic paper detailing theoretical results in machine learning.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Thompson Sampling Proven 2-Competitive for Mistakes in Bayesian Bandit Models

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Mark Sellke, Gregory Valiant ·

    Thompson Sampling Is 2-Competitive for Mistakes

    arXiv:2607.12389v1 Announce Type: new Abstract: We consider Bayesian bandit models and prove that Thompson sampling makes at most twice the expected number of mistakes (selections of a suboptimal arm) as any other policy. Our analysis applies as long as the latent arm processes a…

  2. arXiv stat.ML TIER_1 English(EN) · Gregory Valiant ·

    Thompson Sampling Is 2-Competitive for Mistakes

    We consider Bayesian bandit models and prove that Thompson sampling makes at most twice the expected number of mistakes (selections of a suboptimal arm) as any other policy. Our analysis applies as long as the latent arm processes are independent and each arm evolves only when pl…

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