mlsanityData to 7 Oct 2026

Data-Driven Robust Optimization

arXiv 1401.0212 ↗First posted 31 Dec 2013math.OCcited by 84

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Abstract (arXiv)

The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests. The approach is flexible and widely applicable, and robust optimization problems built from our new sets are computationally tractable, both theoretically and practically. Furthermore, optimal solutions to these problems enjoy a strong, finite-sample probabilistic guarantee. editWe describe concrete procedures for choosing an appropriate set for a given application and applying our approach to multiple uncertain constraints. Computational evidence in portfolio management and queuing confirm that our data-driven sets significantly outperform traditional robust optimization techniques whenever data is available.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Dec 2013.

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Citing papers per year: 2021: 16; 2022: 20; 2023: 16; 2024: 12; 2025: 11; 2026: 9.

84 papers.

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  1. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 431
  2. 2022Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexitycited by 85
  3. 2021Distributional Robustness Loss for Long-tail Learningcited by 84
  4. 2023The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Modelcited by 50
  5. 2021Conformal Uncertainty Sets for Robust Optimizationcited by 49
  6. 2023Diagnosing Model Performance Under Distribution Shiftcited by 37
  7. 2022Distributionally Robust Offline Reinforcement Learning with Linear Function Approximationcited by 31
  8. 2021Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptoticscited by 27
  9. 2021Simple and near-optimal algorithms for hidden stratification and multi-group learningcited by 26
  10. 2021Integrated Conditional Estimation-Optimizationcited by 25
  11. 2023Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualizationcited by 25
  12. 2023Conformal Contextual Robust Optimizationcited by 25
38 more
  1. 2023A Survey on the Robustness of Computer Vision Models against Common Corruptionscited by 20
  2. 2024Adaptive Preference Scaling for Reinforcement Learning with Human Feedbackcited by 20
  3. 2021Distributionally Robust Multilingual Machine Translationcited by 18
  4. 2023Simplifying Neural Network Training Under Class Imbalancecited by 18
  5. 2022Maximizing information from chemical engineering data sets: Applications to machine learningcited by 17
  6. 2022Distributionally Robust End-to-End Portfolio Constructioncited by 17
  7. 2022Holistic Robust Data-Driven Decisionscited by 17
  8. 2022Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruningcited by 17
  9. 2022Stochastic Constrained DRO with a Complexity Independent of Sample Sizecited by 16
  10. 2022Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyondcited by 16
  11. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  12. 2021Distributionally Robust Learning with Stable Adversarial Trainingcited by 14
  13. 2023Distributionally Robust Optimization with Probabilistic Groupcited by 14
  14. 2024End-to-End Conformal Calibration for Optimization Under Uncertaintycited by 14
  15. 2021Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal controlcited by 12
  16. 2023Distributionally Robust Recourse Actioncited by 12
  17. 2023Unifying Distributionally Robust Optimization via Optimal Transport Theorycited by 12
  18. 2021The s-value: evaluating stability with respect to distributional shiftscited by 10
  19. 2023Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shiftscited by 9
  20. 2023HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shiftscited by 8
  21. 2025What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimizationcited by 8
  22. 2021Learning and Decision-Making with Data: Optimal Formulations and Phase Transitionscited by 7
  23. 2023Online Resource Allocation with Convex-set Machine-Learned Advicecited by 7
  24. 2021Robust Generalization despite Distribution Shift via Minimum Discriminating Informationcited by 6
  25. 2022Learning Distributionally Robust Models at Scale via Composite Optimizationcited by 5
  26. 2024Evaluating Model Performance Under Worst-case Subpopulationscited by 5
  27. 2024DRPruning: Efficient Large Language Model Pruning through Distributionally Robust Optimizationcited by 5
  28. 2024Toward Robust Neural Reconstruction from Sparse Point Setscited by 5
  29. 2025Enhanced Route Planning with Calibrated Uncertainty Setcited by 5
  30. 2021Distributionally Robust Optimization with Markovian Datacited by 4
  31. 2022On Certifying and Improving Generalization to Unseen Domainscited by 4
  32. 2021Learning Under Adversarial and Interventional Shiftscited by 3
  33. 2022Wasserstein Distributionally Robust Optimization with Wasserstein Barycenterscited by 3
  34. 2024Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Setscited by 3
  35. 2025Causality-Inspired Robustness for Nonlinear Models via Representation Learningcited by 3
  36. 2022Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimizationcited by 2
  37. 2022On data-driven chance constraint learning for mixed-integer optimization problemscited by 2
  38. 2022Regret Bounds and Experimental Design for Estimate-then-Optimizecited by 2

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