论文标题
通过Frank-Wolfe快速学习多维鹰队的过程
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
论文作者
论文摘要
在建模和生成顺序事件数据方面，霍克斯的过程最近已升至工具的最前沿。多维霍克斯的过程模拟了不同类型事件之间的自我和交叉兴趣，并已成功应用于各种领域，例如财务，流行病学和个性化建议等。在这项工作中，我们介绍了Frank-Wolfe算法的改编，用于学习多维霍克斯流程。实验结果表明，与其他一阶方法相比，在参数估计方面，我们的方法的准确性更好或具有标准级，同时享受运行时的速度明显更快。
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.
