论文标题
网络瓶颈标识的上下文组合半一样的方法
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification
论文作者
论文摘要
瓶颈标识是网络分析中的一项具有挑战性的任务，尤其是当网络未完全指定时。为了解决此任务，我们基于组合半伴侣制定了一个统一的在线学习框架，该框架与学习基础网络的规格并行执行瓶颈识别。在此框架内，我们适应和研究各种组合半伴侣方法，例如epsilon-greedy，linucb，bayeasucb，neuralucb和Thompson采样。此外，我们的框架能够以上下文匪徒的形式使用上下文信息。最后，我们在道路网络的实际应用上评估了我们的框架，并在不同的环境中展示了其有效性。
Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified. To address this task, we develop a unified online learning framework based on combinatorial semi-bandits that performs bottleneck identification in parallel with learning the specifications of the underlying network. Within this framework, we adapt and study various combinatorial semi-bandit methods such as epsilon-greedy, LinUCB, BayesUCB, NeuralUCB, and Thompson Sampling. In addition, our framework is capable of using contextual information in the form of contextual bandits. Finally, we evaluate our framework on the real-world application of road networks and demonstrate its effectiveness in different settings.
