  As machine learning tasks continue to evolve, the trend has been to gather
larger datasets and train increasingly larger models. While this has led to
advancements in accuracy, it has also escalated computational costs to
unsustainable levels. Addressing this, our work aims to strike a delicate
balance between computational efficiency and model accuracy, a persisting
challenge in the field. We introduce a novel method that employs core subset
selection for reweighting, effectively optimizing both computational time and
model performance. By focusing on a strategically selected coreset, our
approach offers a robust representation, as it efficiently minimizes the
influence of outliers. The re-calibrated weights are then mapped back to and
propagated across the entire dataset. Our experimental results substantiate the
effectiveness of this approach, underscoring its potential as a scalable and
precise solution for model training.
