# QueryMarket: Cost-Aware Online Active Learning in Data Markets

arXiv: https://arxiv.org/abs/2606.17805 · HTML: https://mlsanity.com/paper/2606.17805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 35 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [Data Valuation using Reinforcement Learning](https://mlsanity.com/paper/1909.11671/) (2019)
- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Efficient Second Order Online Learning by Sketching](https://mlsanity.com/paper/1602.02202/) (2016)
- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance Assessment](https://mlsanity.com/paper/2301.10625/) (2023)
- [Streaming Active Learning with Deep Neural Networks](https://mlsanity.com/paper/2303.02535/) (2023)
- [Fundamentals of Task-Agnostic Data Valuation](https://mlsanity.com/paper/2208.12354/) (2022)
- [Regression markets and application to energy forecasting](https://mlsanity.com/paper/2110.03633/) (2021)
- [Stream-based active learning with linear models](https://mlsanity.com/paper/2207.09874/) (2022)
- [Active Regression by Stratification](https://mlsanity.com/paper/1410.5920/) (2014)
- [Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders](https://mlsanity.com/paper/2212.13067/) (2022)
- [Online Active Linear Regression via Thresholding](https://mlsanity.com/paper/1602.02845/) (2016)
- [Strategic Coalition for Data Pricing in IoT Data Markets](https://mlsanity.com/paper/2206.07785/) (2022)
- [Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical Guarantees](https://mlsanity.com/paper/2402.02110/) (2024)
- [Budgeted Online Active Learning with Expert Advice and Episodic Priors](https://mlsanity.com/paper/2506.03307/) (2025)
- [How to Purchase Labels? A Cost-Effective Approach Using Active Learning Markets](https://mlsanity.com/paper/2511.20605/) (2025)

## Abstract (arXiv)

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.