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This paper introduces O-MPAC, a novel machine learning framework designed to optimize feature group selection under strict acquisition budgets by leveraging offline planning techniques. The method effectively transfers risk-cost targets from complete training data into a dynamic scorer that adapts to partial observations and source metadata during inference. By outperforming existing baselines like GDFS and DIME on real-world tasks, the research advances the state of cost-aware sequential decision-making in machine learning systems.
Learning Source Acquisition Policies by Offline Planning
arXiv:2609.14299v1 Announce Type: new Abstract: Predicting under an acquisition budget requires choosing feature groups whose value can depend on later queries. O-MPAC transfers finite-horizon risk-cost targets from complete training records into a shared source-action scorer. At inference time, the scorer uses partial observations and source metadata, re-scores after each query, and applies a hard cost mask. We analyze how tied teacher targets and the remaining planning horizon affect the learned decisions. Uniform supervision over tied minima preserves the target distribution under source relabeling. In a five-seed routing experiment, it achieves 0.965 accuracy under both original and context-last orders. On six real tasks, validation selects H1 without action cross-entropy in all thirty splits. O-MPAC has the highest mean budget-integrated accuracy on five tasks against source-adapted GDFS, DIME, AACO+NN and a static policy.
Abstract:Predicting under an acquisition budget requires choosing feature groups whose value can depend on later queries. O-MPAC transfers finite-horizon risk-cost targets from complete training records into a shared source-action scorer. At inference time, the scorer uses partial observations and source metadata, re-scores after each query, and applies a hard cost mask. We analyze how tied teacher targets and the remaining planning horizon affect the learned decisions. Uniform supervision over tied minima preserves the target distribution under source relabeling. In a five-seed routing experiment, it achieves 0.965 accuracy under both original and context-last orders. On six real tasks, validation selects H1 without action cross-entropy in all thirty splits. O-MPAC has the highest mean budget-integrated accuracy on five tasks against source-adapted GDFS, DIME, AACO+NN and a static policy.
Machine Learning (cs.LG)
arXiv:2609.14299 [cs.LG]
(or arXiv:2609.14299v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.14299 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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