{"id":12076,"date":"2026-07-20T10:17:13","date_gmt":"2026-07-20T15:17:13","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/annual\/?page_id=12076"},"modified":"2026-07-20T10:41:31","modified_gmt":"2026-07-20T15:41:31","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/annual\/tutorials\/","title":{"rendered":"TutORials"},"content":{"rendered":"\n
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TutORials<\/h1> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n
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The TutORials in Operations Research<\/em> series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new developments in the field and serves as augmenting material for a selection of the tutorial presentations offered at the INFORMS Annual Meeting.<\/p> <\/div>\n<\/div>\n <\/div>\n

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Generative Models for Decision-Making under Distributional Shift<\/h3>\n
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 flowand
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\u2013
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 guarantees, including forward\u2013reverse convergence for
iterative flow models, first-order minimax analysis in transport-map space, and errortransfer
bounds for posterior sampling with generative priors. The tutorial provides
a principled introduction to using generative models for scenario generation, robust
decision-making, uncertainty quantification, and related problems under distributional
shift.<\/div>\n
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Speakers: Xiuyuan Cheng, Yunqin Zhu, Yao Xie <\/strong><\/p> <\/div>\n<\/div>\n\n

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