arXiv:2510.12624v2 Announce Type: replace Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire. In practice, AFA methods often learn from retrospective data with systematic missingness in the features and limited task-specific labels. Most prior work addresses acquisition for a single predetermined task, limiting scalability. To address this limitation, we formalize the meta-AFA problem, where the goal is to learn acquisition policies across various
The proliferation of AI models across diverse applications necessitates more efficient and scalable data acquisition methods, pushing research towards context-aware and meta-learning approaches.
This research addresses a fundamental bottleneck in AI deployment by proposing a more adaptive and scalable way to acquire relevant features, crucial for generalizing AI across numerous real-world tasks.
The focus is shifting from single-task feature acquisition to meta-learning acquisition policies, enabling AI systems to more effectively learn and adapt in complex, data-scarce, or rapidly changing environments.
- · AI model developers
- · Data-intensive industries
- · Specialized AI applications
- · Edge AI providers
- · Static data acquisition firms
- · High-cost manual feature engineering
Improved efficiency and generalization of AI models, especially in new or diverse task settings.
Reduced data acquisition costs and faster deployment timelines for AI solutions, broadening AI's applicability.
Acceleration of autonomous AI agents capable of self-optimizing their data intake and decision-making for complex, real-world problems.
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Read at arXiv cs.LG