This is a report for reproducibility challenge of NeurlIPS 2019 on the paper Competitive Gradient Descent (Schafer et al., 2019). The paper introduces a novel algorithm for the numerical computation of Nash equilibria of competitive two-player games. It avoids oscillatory and divergent behaviours seen in alternating gradient descent. The purpose of this report is to critically examine the reproducibility of the work by (Schafer et al., 2019), within the framework of the NeurIPS 2019 Reproducibility Challenge. The experiments replicated in this report confirms the results of the original study. Moreover, this project offers a Python (Pytorch based) implementation of the proposed CGD algorithm which can be found at the following public git repository: (https://github.com/GopiKishan14/Reproducibility_Challenge_NeurIPS_2019)
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
McGill University（麦吉尔大学）；Facebook AI Research（Facebook人工智能研究院）；CIFAR（加拿大高等研究院）；Université de Montréal（蒙特利尔大学）；Yahoo! Research（雅虎研究院）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de France（法国理工学院）；University of Washington（华盛顿大学）；Apple（苹果公司）；Google（谷歌）
Communication-Efficient Distributed SGD with Error-Feedback, Revisited
Ton Duc Thang University（孙德胜大学）；Meiji University（明治大学）；NICT（日本国立信息通信技术研究所）；National Institute of Information and Communications Technology (NICT)（日本国立信息通信技术研究所）
University of Southern California（南加州大学）；Iterable, Inc.（Iterable公司）；University of Virginia（弗吉尼亚大学）；University of Waterloo（滑铁卢大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；RISE - Research Institutes of Sweden, SICS（瑞典研究院SICS）
