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New O-MPAC method optimizes source acquisition policies with offline planning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed O-MPAC, a novel method for optimizing source acquisition policies under budget constraints. This approach transfers finite-horizon risk-cost targets from complete training records into a shared scorer, which then re-scores based on partial observations and source metadata during inference. Experiments show O-MPAC achieves high accuracy across various ordering scenarios and outperforms existing methods on real-world tasks. AI

IMPACT Introduces a new planning method for optimizing data acquisition in machine learning systems.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new method for optimizing source acquisition policies. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New O-MPAC method optimizes source acquisition policies with offline planning

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The cluster contains a research paper published on arXiv detailing a new method for optimizing source acquisition policies. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ziqi Zhao, Run Xu, Qingjian Ni ·

    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…

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