Wasserstein Distributionally Robust Optimization and Variation Regularization
建立了Wasserstein分布鲁棒性与变分正则化之间的通用联系，解释了Wasserstein分布鲁棒优化的经验成功，并为机器学习设计了新的正则化方案。
This paper builds a bridge between two area in optimization and machine learning by establishing a general connection between Wasserstein distributional robustness and variation regularization. It helps to demystify the empirical success of Wasserstein distributionally robust optimization and devise new regularization schemes for machine learning.