Data-driven Optimal Cost Selection for Distributionally Robust Optimization

19 May 2017Jose BlanchetYang KangFan ZhangKarthyek Murthy

Recently, (Blanchet, Kang, and Murhy 2016, and Blanchet, and Kang 2017) showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) problems. The distributional uncertainty is defined as a neighborhood centered at the empirical distribution... (read more)

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