论文标题
MLOSP：统一实施蒙特卡洛算法的回归
mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms
论文作者
论文摘要
我们介绍了MLOSP，这是一种用于机器学习的计算模板，用于最佳停止问题。该模板在R统计环境中实现，并通过GITHUB存储库公开获得。 MLOSP介绍了回归蒙特卡洛（RMC）方法的统一数值实施，以提供最佳的停止，提供最先进的，开源，可再现和透明的平台。我们介绍了RMC算法的多种新颖变体，尤其是在构建用于训练回归器的模拟设计以及机器学习回归模块方面。此外，MLOSP嵌套了大多数现有的RMC方案，从而允许对现有算法进行一致且可验证的基准测试。本文包含大量的R代码片段和数字，并用作基础软件包的小插图。
We introduce mlOSP, a computational template for Machine Learning for Optimal Stopping Problems. The template is implemented in the R statistical environment and publicly available via a GitHub repository. mlOSP presents a unified numerical implementation of Regression Monte Carlo (RMC) approaches to optimal stopping, providing a state-of-the-art, open-source, reproducible and transparent platform. Highlighting its modular nature, we present multiple novel variants of RMC algorithms, especially in terms of constructing simulation designs for training the regressors, as well as in terms of machine learning regression modules. Furthermore, mlOSP nests most of the existing RMC schemes, allowing for a consistent and verifiable benchmarking of extant algorithms. The article contains extensive R code snippets and figures, and serves as a vignette to the underlying software package.
