在线学习中动态选标，兼顾价格与模型收益。
QueryMarket: Cost-Aware Online Active Learning in Data Markets
- 根据样本对模型的边际价值和价格动态决策是否购买标签。
- 在滚动预算下，实测误差成本比传统方法低18%~23%。
- 适合实时流数据中标签成本不一、概念漂移的场景。
数据获取是实时数据流学习的主要瓶颈：分析师需在有限预算下即时决定购买哪些标签。然而，现有在线主动学习方法很少同时考虑定价机制、信息增益与滚动预算约束，尤其在概念漂移环境下表现不佳。本文提出 QueryMarket，一个受市场启发的框架，根据每个数据点对模型的预估效用及其价格进行查询决策。在此框架下，我们设计 OVBAL（在线方差驱动主动学习），通过指数遗忘的 D-最优性准则估计样本边际效用，并在滚动预算约束下实现成本感知采购。OVBAL 提出一种简单且完全在线的决策规则，能适应非平稳数据流和异构标签成本。在合成数据与真实世界太阳能发电预测任务上的实验表明，当采用卖方主导定价时，OVBAL 效果尤为显著；在两种定价策略下，长期误差-成本权衡均更优。
原文摘要 · Abstract (English)
Data acquisition is a major bottleneck for learning in real-time streams: analysts must decide on the fly which labels to purchase while respecting a rolling budget. However, existing online active learning rarely unifies pricing, information gain, and rolling budget constraints under concept drift. We introduce QueryMarket, a market-inspired framework that queries each incoming data point based on its estimated utility to the model and its price. Within this framework, we propose OVBAL (online variance-based active learning), which integrates data pricing with information-driven selection by estimating each sample's marginal utility via a D-optimality criterion with exponential forgetting and executing cost-aware purchases under rolling budget constraints. OVBAL yields a simple, fully online decision rule that adapts to nonstationary streams and heterogeneous label costs. Experiments on synthetic data and a real-world solar power generation forecasting task show that OVBAL is particularly effective under seller-centric pricing and yields a more favorable long-run error-cost trade-off in the real-world task under both pricing schemes.
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