Distributionally Robust Optimization: A Review

Abstract

The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization.

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• A Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification
• Distributionally Robust Model Order Reduction for Linear Systems
• Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity
• Multi-Hypothesis Prediction for Portfolio Optimization: A Structured Ensemble Learning Approach to Risk Diversification