QueryMarket: Cost-Aware Online Active Learning in Data Markets

Abstract: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.

10 pages, 8 figures. Submitted to IEEE Transactions on Neural Networks and Learning Systems

Machine Learning (cs.LG)

arXiv:2606.17805 [cs.LG]

(or arXiv:2606.17805v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.17805 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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