Optimism Stabilizes Thompson Sampling for Adaptive Inference

A new arXiv paper introduces a method called "optimism" to stabilize Thompson sampling, a technique widely used in adaptive inference for multi-armed bandit problems. The research, led by Han Zhong, demonstrates that this "optimism" mechanism ensures that arm-specific sample sizes concentrate around a deterministic scale, which in turn allows for asymptotically valid Wald inference. This stabilization is achieved through new winner-map and Lyapunov-drift techniques, resolving a previously open question regarding the extension of this method to K-armed bandits. AI

IMPACT Introduces a theoretical advancement for adaptive inference in multi-armed bandit problems, potentially improving decision-making in systems that learn from interaction.

RANK_REASON The cluster contains a new academic paper published on arXiv detailing a novel method for adaptive inference in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
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• DagsHub
• Gaussian randomized indices
• Gotit.pub
• halder2025stable
• Han Zhong
• Hugging Face
• K-armed stochastic bandits
• Lyapunov Drift Conditions for General Symmetric Jump Processes
• ScienceCast
• Thompson sampling
• Wald inference

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