Data Budgeting

Data budgeting in machine learning focuses on optimizing data acquisition and utilization by predicting the performance gains from additional data and determining the minimal data needed for satisfactory model performance. Current research explores learning-based methods to estimate these quantities, moving beyond dataset-independent approaches, and investigates how to achieve robust model performance even with limited data, employing architectures like ResNets. This field is crucial for making AI development more efficient and cost-effective, particularly in resource-constrained settings, and for improving the reliability of AI models under real-world conditions.