论文标题
使用贝叶斯内核嵌入对无与伦比数据的顺序决策
Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings
论文作者
论文摘要
依次最大化函数期望的问题旨在最大程度地提高感兴趣函数的预期值，而无需直接控制其功能。相反，此类特征的分布取决于给定的上下文和代理商采取的动作。与贝叶斯优化相反，该函数的参数不在代理的控制之下，而是根据给定上下文的代理人的行动间接确定的。如果将功能的信息包括在最大化问题中，则需要考虑此类功能的全部条件分布，而不是仅期望。此外，该函数本身是未知的，只有对这种函数的嘈杂观察，并且可能需要使用无与伦比的数据集。我们为上述问题提出了一种新型算法，该算法考虑到特征的条件分布和未知功能的估计得出的不确定性，通过将前者建模为贝叶斯条件均值嵌入，后者将前者建模为高斯过程。我们的算法在经验上优于进行实验中的当前最新算法。
The problem of sequentially maximizing the expectation of a function seeks to maximize the expected value of a function of interest without having direct control on its features. Instead, the distribution of such features depends on a given context and an action taken by an agent. In contrast to Bayesian optimization, the arguments of the function are not under agent's control, but are indirectly determined by the agent's action based on a given context. If the information of the features is to be included in the maximization problem, the full conditional distribution of such features, rather than its expectation only, needs to be accounted for. Furthermore, the function is itself unknown, only counting with noisy observations of such function, and potentially requiring the use of unmatched data sets. We propose a novel algorithm for the aforementioned problem which takes into consideration the uncertainty derived from the estimation of both the conditional distribution of the features and the unknown function, by modeling the former as a Bayesian conditional mean embedding and the latter as a Gaussian process. Our algorithm empirically outperforms the current state-of-the-art algorithm in the experiments conducted.
