Learning-To-Measure: In-Context Active Feature Acquisition
📰 ArXiv cs.AI
Learn to improve model performance by actively acquiring features in context, a crucial skill for AI engineers and data scientists
Action Steps
- Apply active feature acquisition (AFA) to improve model performance on test instances
- Use in-context learning to adaptively select features to acquire
- Configure AFA methods to learn from retrospective data with systematic missingness
- Test the performance of AFA methods on multiple tasks to evaluate scalability
- Compare the results of different AFA methods to determine the most effective approach
Who Needs to Know This
AI engineers and data scientists can benefit from this technique to enhance model performance, especially when working with limited task-specific labels and systematic missingness in features
Key Insight
💡 Active feature acquisition can improve model performance by adaptively selecting features to acquire, especially in cases with limited task-specific labels and systematic missingness
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🤖 Improve model performance with active feature acquisition! 📈 Learn to adaptively select features in context 📊 #AI #MachineLearning
Key Takeaways
Learn to improve model performance by actively acquiring features in context, a crucial skill for AI engineers and data scientists
Full Article
Title: Learning-To-Measure: In-Context Active Feature Acquisition
Abstract:
arXiv:2510.12624v2 Announce Type: replace-cross 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 limi
Abstract:
arXiv:2510.12624v2 Announce Type: replace-cross 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 limi
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