论文标题
遗憾的无噪声基于内核的土匪的界限
Regret Bounds for Noise-Free Kernel-Based Bandits
论文作者
论文摘要
基于内核的Bandit是一个广泛研究的黑盒优化问题，其中假定目标函数生活在已知的繁殖核Hilbert空间中。尽管在嘈杂的环境中建立了几乎最佳的遗憾界限（达到对数因素），但令人惊讶的是，对于无噪声设置（如果没有观察噪声而可以访问基础函数的确切值）。我们遗憾地讨论了几个上限。这些似乎都没有最佳秩序，并在最佳遗憾中提供了猜想。
Kernel-based bandit is an extensively studied black-box optimization problem, in which the objective function is assumed to live in a known reproducing kernel Hilbert space. While nearly optimal regret bounds (up to logarithmic factors) are established in the noisy setting, surprisingly, less is known about the noise-free setting (when the exact values of the underlying function is accessible without observation noise). We discuss several upper bounds on regret; none of which seem order optimal, and provide a conjecture on the order optimal regret bound.
