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.

References in corpus (59)

• JuMP: A Modeling Language for Mathematical Optimization
• A Practical Guide to Robust Optimization
• Game theory, maximum entropy, minimum discrepancy and robust Bayesian decision theory
• Certifying Some Distributional Robustness with Principled Adversarial Training
• Robustness and Regularization of Support Vector Machines
• Robust Wasserstein Profile Inference and Applications to Machine Learning
• Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls
• Distributionally Robust Logistic Regression
• A data-driven robust optimization approach to scenario-based stochastic model predictive control
• Consistency and robustness of kernel-based regression in convex risk minimization
• Wasserstein Distributionally Robust Stochastic Control: A Data-Driven Approach
• Regularization via Mass Transportation
• Does Distributionally Robust Supervised Learning Give Robust Classifiers?
• Robust Model Predictive Control of Irrigation Systems with Active Uncertainty Learning and Data Analytics
• Distributionally Robust Stochastic Optimization with Wasserstein Distance
• Ambiguous Chance-Constrained Binary Programs under Mean-Covariance Information
• Minimax Statistical Learning with Wasserstein Distances
• Robust Hypothesis Testing with a Relative Entropy Tolerance
• A dynamic game approach to distributionally robust safety specifications for stochastic systems
• Data-Driven Chance Constrained Programs over Wasserstein Balls
• Tail Analysis without Parametric Models: A Worst-case Perspective
• Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
• Sample Out-Of-Sample Inference Based on Wasserstein Distance
• Distributionally Robust Stochastic Optimization with Dependence Structure
• Wasserstein Distributionally Robust Kalman Filtering
• Minimax Distribution Estimation in Wasserstein Distance
• Optimal Transport Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes
• On Distributionally Robust Chance Constrained Programs with Wasserstein Distance
• Robust Optimization using Machine Learning for Uncertainty Sets
• Data-Driven Sample Average Approximation with Covariate Information
• Time (in)consistency of multistage distributionally robust inventory models with moment constraints
• Distributionally Robust Reinforcement Learning
• Distributionally robust polynomial chance-constraints under mixture ambiguity sets
• Distributional Robustness and Regularization in Reinforcement Learning
• Distributionally Robust Optimization and Generalization in Kernel Methods
• Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty under Wasserstein Ambiguity
• Distributionally robust inventory control when demand is a martingale
• A Study of Distributionally Robust Multistage Stochastic Optimization
• On Data-Driven Prescriptive Analytics with Side Information: A Regularized Nadaraya-Watson Approach
• Incorporating statistical model error into the calculation of acceptability prices of contingent claims
• Distributionally Robust Optimization with Decision Dependent Ambiguity Sets
• Robustifying Conditional Portfolio Decisions via Optimal Transport
• Data-driven Optimal Cost Selection for Distributionally Robust Optimization
• Doubly Robust Data-Driven Distributionally Robust Optimization
• Multistage Distributionally Robust Mixed-Integer Programming with Decision-Dependent Moment-Based Ambiguity Sets
• Residuals-based distributionally robust optimization with covariate information
• Strong Formulations for Distributionally Robust Chance-Constrained Programs with Left-Hand Side Uncertainty under Wasserstein Ambiguity
• Distributionally Robust Groupwise Regularization Estimator
• Efficient Algorithms for Distributionally Robust Stochastic Optimization with Discrete Scenario Support
• The Minimum Information Principle for Discriminative Learning
• Distributionally Robust Counterpart in Markov Decision Processes
• The Empirical Likelihood Approach to Quantifying Uncertainty in Sample Average Approximation
• A Composite Risk Measure Framework for Decision Making under Uncertainty
• Discrete Approximation of Two-Stage Stochastic and Distributionally Robust Linear Complementarity Problems
• Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
• ROC++: Robust Optimization in C++
• Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
• Asymptotically Minimax Robust Hypothesis Testing
• A Multistage Distributionally Robust Optimization Approach to Water Allocation under Climate Uncertainty

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