Generative Distributionally Robust Optimization

arXiv:2607.24983 · cs.LG, cs.AI, math.OC, stat.ML · Submitted 2026-07-27 · Read on arXiv

Ziwei Zhang, Jonathan Yu-Meng Li, Zhihao Jin

cs.LG, cs.AI, math.OC, stat.ML

Submitted: 2026-07-27

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generator — 72× in this paper · explained in 2 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Regret — 11× in this paper · explained in 4 episodes
• Robustness — 10× in this paper · explained in 6 episodes
• Generative Distributionally Robust Optimization — 7× in this paper
• Generative Model — 7× in this paper · explained in 2 episodes
• Decoder — 6× in this paper · explained in 2 episodes
• Generative Models — 6× in this paper · explained in 3 episodes
• Optimal Transport — 5× in this paper · explained in 5 episodes
• Distributionally Robust Optimization (DRO) — 3× in this paper · explained in 3 episodes
• Papers — 3× in this paper · explained in 3 episodes
• Adversarial Model — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Latent Variables — 2× in this paper · explained in 2 episodes
• Normalizing Flow — 2× in this paper · explained in 4 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Inflation — 1× in this paper · explained in 2 episodes
• Kullback–Leibler Divergence — 1× in this paper · explained in 2 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Regret Bound — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Training Objective — 1× in this paper · explained in 3 episodes
• Wasserstein Distance — 1× in this paper · explained in 6 episodes
• Weight Decay — 1× in this paper · explained in 2 episodes

Sources

• Distributionally Robust Optimization
• Distributionally Robust Policy Learning via Adversarial Environment Generation
• Learning Decision-Focused Uncertainty Sets in Robust Optimization
• Distributionally Robust Optimization via Diffusion Ambiguity Modeling
• Nested Stochastic Algorithm for Generalized Sinkhorn distance-Regularized Distributionally Robust Optimization

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