AI RESEARCH
Stochastic set-valued optimization and its application to robust learning
arXiv CS.LG
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ArXi:2603.17691v1 Announce Type: cross In this paper, we develop a stochastic set-valued optimization (SVO) framework tailored for robust machine learning. In the SVO setting, each decision variable is mapped to a set of objective values, and optimality is defined via set relations. We focus on SVO problems with hyperbox sets, which can be reformulated as multi-objective optimization (MOO) problems with finitely many objectives and serve as a foundation for representing or approximating general mapped sets.