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Optimism Stabilizes Thompson Sampling for Adaptive Inference

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的arXiv论文介绍了一种名为“乐观主义”的方法来稳定Thompson采样，这是一种广泛用于多臂老虎机问题自适应推理的技术。由Han Zhong领导的研究表明，这种“乐观主义”机制确保了臂特定样本量集中在确定性尺度周围，从而允许渐近有效的Wald推理。这种稳定是通过新的获胜图和Lyapunov漂移技术实现的，解决了先前关于将此方法扩展到K臂老虎机问题的开放性问题。 AI

影响 为多臂老虎机问题的自适应推理带来了理论上的进步，有可能改善从交互中学习的系统的决策。

排序理由 该集群包含一篇发表在arXiv上的新学术论文，详细介绍了一种用于机器学习自适应推理的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]

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Optimism Stabilizes Thompson Sampling for Adaptive Inference

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该集群包含一篇发表在arXiv上的新学术论文，详细介绍了一种用于机器学习自适应推理的新颖方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Shunxing Yan, Han Zhong ·

    Optimism Stabilizes Thompson Sampling for Adaptive Inference

    arXiv:2602.06014v2 Announce Type: replace-cross Abstract: Thompson sampling (TS) is widely used for stochastic multi-armed bandits, yet its inferential properties under adaptive data collection are subtle. Classical asymptotic theory for sample means can fail because arm-specific…

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