Daniel Kuhn
21/03/17 16:41
Data-Driven Distributionally Robust Optimization Using the Wasserstein Metric
Abstract: We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of probability distributions centered at the uniform distribution on the training samples, and we seek decisions that perform best in view of the worst-case distribution within this ball. We show that the resulting optimization problems can be solved efficiently and that their solutions enjoy powerful out-of-sample performance guarantees on test data. The wide applicability of this approach is illustrated with examples in portfolio selection, uncertainty quantification, statistical learning and inverse optimization.
Daniel Kuhn holds the Chair of Risk Analytics and Optimization at EPFL. Before joining EPFL, he was a faculty member at Imperial College London (2007-2013) and a postdoctoral researcher at Stanford University (2005-2006). He received a PhD in Economics from the University of St. Gallen in 2004 and an MSc in Theoretical Physics from ETH Zurich in 1999. His research interests revolve around robust optimization and stochastic programming.