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.
- arXiv
- Bayesian bandit models
- Thompson sampling
- alphaXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- ScienceCast
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