English(EN) Sharper Regret Bounds for Time-Varying Gaussian Process Bandits with Constant Exploration

新研究改进高斯过程老虎机优化技术 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] · 2026-08-18 04:00

arXiv上的两篇新研究论文探讨了高斯过程老虎机优化方面的进展。第一篇论文侧重于随时间变化的环境，提出了一种具有恒定探索参数的方法，以实现更优的遗憾界限。第二篇论文解决了并行高斯过程老虎机优化问题，展示了如何在不要求初始不确定性采样阶段的情况下，尤其是在无噪声设置中，实现更优的遗憾界限。 AI

影响这些理论上的进步可能导致更高效的优化任务AI系统在动态环境中。

排序理由 arXiv上发表的两篇学术论文，详细介绍了机器学习算法的理论进展。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv stat.ML TIER_1 English(EN) · Matthias Mandl, Hanne Kekkonen · 2026-08-20 04:00

具有恒定探索的随时间变化的高斯过程老虎机更优的遗憾界限

arXiv:2608.18863v1 Announce Type: new Abstract: We study Bayesian optimization in a time-varying environment where the unknown reward function evolves according to a Gaussian process drift model. Existing GP-UCB analyses in this setting typically require the exploration parameter…
arXiv stat.ML TIER_1 English(EN) · Shion Takeno, Shogo Iwazaki · 2026-08-18 04:00

并行高斯过程赌徒优化改进的遗憾分析

arXiv:2608.16492v1 Announce Type: new Abstract: This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP batched upper confidence bound and GP batched Thompson sampling (GP-BTS) suffer from…