arXiv:2606.17805v1 Announce Type: new 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 da
The increasing scale and cost of data for AI models, especially in real-time or dynamic environments, necessitates more efficient data acquisition strategies.
Efficient and cost-aware data acquisition in online learning directly impacts the development and deployment of more agile and cost-effective AI systems, crucial for competitive advantage.
Data market participants will have a framework to optimize data purchasing under budget constraints, integrating pricing and information gain, which will influence how AI models are trained and updated.
- · AI developers
- · Data market platforms
- · Organizations with limited AI budgets
- · Researchers in active learning
- · Inefficient data providers
- · AI projects with uncontrolled data costs
Increased efficiency in active learning data acquisition for dynamic AI systems.
Faster and cheaper development of robust AI models adaptable to concept drift.
Democratization of advanced AI capabilities as data acquisition becomes more accessible and cost-effective for a wider range of organizations.
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Read at arXiv cs.LG