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[2602.19578] Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization
Summary
The paper presents Goal-Oriented Influence-Maximizing Data Acquisition (GOIMDA), a novel algorithm for active data acquisition in machine learning that enhances performance while reducing the need for labeled samples.
Why It Matters
This research addresses the challenges of active data acquisition in machine learning by proposing a method that maximizes the influence of data on specific goals, which can lead to more efficient learning processes. The implications for reducing sample requirements are significant for various applications in AI and optimization.
Key Takeaways
- GOIMDA avoids explicit posterior inference while remaining uncertainty-aware.
- The algorithm maximizes expected influence on user-defined goals like test loss.
- Empirical results show GOIMDA outperforms traditional uncertainty-based methods.
- The approach combines goal gradient and training-loss curvature for effective data selection.
- GOIMDA is applicable across various learning and optimization tasks.
Statistics > Machine Learning arXiv:2602.19578 (stat) [Submitted on 23 Feb 2026] Title:Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization Authors:Weichi Yao, Bianca Dumitrascu, Bryan R. Goldsmith, Yixin Wang View a PDF of the paper titled Goal-Oriented Influence-Maximizing Data Acquisition for Learning and Optimization, by Weichi Yao and 3 other authors View PDF HTML (experimental) Abstract:Active data acquisition is central to many learning and optimization tasks in deep neural networks, yet remains challenging because most approaches rely on predictive uncertainty estimates that are difficult to obtain reliably. To this end, we propose Goal-Oriented Influence- Maximizing Data Acquisition (GOIMDA), an active acquisition algorithm that avoids explicit posterior inference while remaining uncertainty-aware through inverse curvature. GOIMDA selects inputs by maximizing their expected influence on a user-specified goal functional, such as test loss, predictive entropy, or the value of an optimizer-recommended design. Leveraging first-order influence functions, we derive a tractable acquisition rule that combines the goal gradient, training-loss curvature, and candidate sensitivity to model parameters. We show theoretically that, for generalized linear models, GOIMDA approximates predictive-entropy minimization up to a correction term accounting for goal alignment and prediction bias, thereby, yielding uncertainty-aware behavior without maintainin...
