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Distributionally Robust Optimization
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Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical information. DRO seeks decisions that perform best under the worst distribution in the ambiguity set. This worst case criterion is supported by findings in psychology and neuroscience, which indicate that many decision-makers have a low tolerance for distributional ambiguity. DRO is rooted in statistics, operations research and control theory, and recent research has uncovered its deep connections to regularization techniques and adversarial training in machine learning. This survey presents the key findings of the field in a unified and self-contained manner.
Forward citations
Cited by 5 Pith papers
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Generative Distributionally Robust Optimization
GDRO pairs arbitrary conditional samplers with Sinkhorn-constrained generator-family adversaries to cut rare-context inventory regret ~60% and SocialGAN collisions ~50% versus nominal decisions.
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Relative Entropy-Bounded Ambiguous Chance Constraints for Robust Planning in Nonlinear Systems
A relative-entropy ambiguity set with a Donsker–Varadhan bound, plus a quadratic-truncation-based estimate of its radius, yields a distributionally robust risk upper bound for nonlinear covariance steering.
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Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
SV-DRO evolves parameter particles via task-optimality-gap Stein gradients inside DRO-MPC, yielding up to 3× higher success on contact-rich manipulation under parametric uncertainty.
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Wasserstein Robust Performative Prediction via Lagrangian Relaxation
A Wasserstein robust performative prediction framework with decision-dependent ambiguity claims linear convergence for two retraining algorithms, but the central convergence proof is not supported by the stated assumptions.
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Improved Stochastic Optimization of LogSumExp
A rescaled SoftPlus family approximates LogSumExp with O(ρ) error, enabling stable stochastic optimization in entropic OT and KL-DRO.
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