论文标题
多变量点过程的噪声对抗性估计
Noise-Contrastive Estimation for Multivariate Point Processes
论文作者
论文摘要
生成模型的对数可能涉及正术和负术语。对于时间多元点过程，负项在每次可能的所有可能事件类型上总和，并在所有可能的时间集成。结果，最大似然估计是昂贵的。我们展示了如何使用噪声对抗性估计的版本 - 一种具有较高随机目标的一般参数估计方法。我们对这个一般思想的特定实例以一种有趣的非平凡的方式发挥了作用，并可以证明其最佳性，一致性和效率可保证。在几个综合和现实世界数据集上，我们的方法显示了好处：为了使模型在固定数据上达到相同级别的日志样式，我们的方法需要较少的功能评估和更少的壁式时间。
The log-likelihood of a generative model often involves both positive and negative terms. For a temporal multivariate point process, the negative term sums over all the possible event types at each time and also integrates over all the possible times. As a result, maximum likelihood estimation is expensive. We show how to instead apply a version of noise-contrastive estimation---a general parameter estimation method with a less expensive stochastic objective. Our specific instantiation of this general idea works out in an interestingly non-trivial way and has provable guarantees for its optimality, consistency and efficiency. On several synthetic and real-world datasets, our method shows benefits: for the model to achieve the same level of log-likelihood on held-out data, our method needs considerably fewer function evaluations and less wall-clock time.
