AI RESEARCH
OTSS: Output-Targeted Soft Segmentation for Contextual Decision-Weight Learning
arXiv CS.LG
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ArXi:2605.00193v1 Announce Type: new Many machine learning systems make constrained decisions by optimizing factorized objectives, but the context-specific objective is often treated as fixed. We study contextual decision-weight learning: from logged decisions and proxy outputs, learn an optimizer-facing weight vector w(x) over interpretable decision factors z(x,d), rather than a direct policy or generic predictive score. We propose OTSS, an output-targeted soft-segmentation model that deploys the personalized decision-ready weight vector.