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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.
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Cited by 10 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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Robust Bayesian Portfolio Optimization with Discrepancy-based Posterior Ambiguity
Introduces feedback-type ambiguity sets for robust Bayesian drift uncertainty in continuous-time portfolio optimization, yielding a modified HJBI equation and classical solution existence for exponential utility via v...
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Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics
SGD is reformulated via a master equation from discrete updates, producing a discrete Fokker-Planck equation that predicts non-stationary variance growth proportional to learning rate in flat Hessian directions.
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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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Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
Introduces a Stein variational inference-based deterministic formulation for distributionally robust control in contact-rich robotic manipulation, reporting up to 3x improved robustness under parametric uncertainty.
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Attribution-Guided Masking for Robust Cross-Domain Sentiment Classification
AGM adds a gradient-based masking loss during fine-tuning to suppress reliance on spurious tokens, achieving competitive zero-shot transfer on sentiment tasks while providing token-level interpretability.
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Robustness Measures in Distributionally Robust Optimization
DRO regularizers are worst-case sensitivities of expected cost, supplying a robustness measure that guides uncertainty-set selection and traces performance-robustness frontiers.
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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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