[2604.04342] Generative models for decision-making under distributional shift

Computer Science > Machine Learning arXiv:2604.04342 (cs) [Submitted on 6 Apr 2026] Title:Generative models for decision-making under distributional shift Authors:Xiuyuan Cheng, Yunqin Zhu, Yao Xie View a PDF of the paper titled Generative models for decision-making under distributional shift, by Xiuyuan Cheng and 2 other authors View PDF HTML (experimental) Abstract:Many data-driven decision problems are formulated using a nominal distribution estimated from historical data, while performance is ultimately determined by a deployment distribution that may be shifted, context-dependent, partially observed, or stress-induced. This tutorial presents modern generative models, particularly flow- and score-based methods, as mathematical tools for constructing decision-relevant distributions. From an operations research perspective, their primary value lies not in unconstrained sample synthesis but in representing and transforming distributions through transport maps, velocity fields, score fields, and guided stochastic dynamics. We present a unified framework based on pushforward maps, continuity, Fokker-Planck equations, Wasserstein geometry, and optimization in probability space. Within this framework, generative models can be used to learn nominal uncertainty, construct stressed or least-favorable distributions for robustness, and produce conditional or posterior distributions under side information and partial observation. We also highlight representative theoretical guarant...