English(EN) Learning Source Acquisition Policies by Offline Planning

新的 O-MPAC 方法通过离线规划优化源获取策略

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了 O-MPAC，一种在预算限制下优化源获取策略的新颖方法。该方法将有限时间范围内的风险成本目标从完整的训练记录转移到一个共享评分器中，然后在推理过程中根据部分观察和源元数据进行重新评分。实验表明，O-MPAC 在各种排序场景下都能实现高精度，并在现实世界任务中优于现有方法。 AI

影响为优化机器学习系统中的数据获取引入了一种新的规划方法。

排序理由该集群包含一篇在 arXiv 上发表的研究论文，详细介绍了一种优化源获取策略的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Ziqi Zhao, Run Xu, Qingjian Ni · 2026-09-15 04:00

通过离线规划学习源获取策略

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…