论文标题
专家的混合分布回归：使用自适应一阶方法的强大估计实施
Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods
论文作者
论文摘要
在这项工作中，我们提出了专家分布回归模型的混合物的有效实施，该模型通过使用随机的一阶优化技术使用自适应学习率调度程序来利用强大的估计。我们利用神经网络软件的灵活性和可扩展性，并在Mixdistreg（R Mixdistreg）中实现了拟议的框架，该框架可以定义许多不同家族的混合物，估计高维和大型样本尺寸设置以及基于TensorFlow的强大优化。使用模拟和现实世界数据应用程序进行的数值实验表明，在许多不同的设置中，优化与经典方法的估计一样可靠，并且对于经典方法始终如一地失败的复杂场景，可以获得结果。
In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adaptive learning rate schedulers. We take advantage of the flexibility and scalability of neural network software and implement the proposed framework in mixdistreg, an R software package that allows for the definition of mixtures of many different families, estimation in high-dimensional and large sample size settings and robust optimization based on TensorFlow. Numerical experiments with simulated and real-world data applications show that optimization is as reliable as estimation via classical approaches in many different settings and that results may be obtained for complicated scenarios where classical approaches consistently fail.
