Sample Weight

Sample weighting, the process of assigning different importance levels to individual data points during model training, is a burgeoning area of research aimed at improving model fairness, robustness, and generalization performance. Current research focuses on developing algorithms that learn optimal sample weights to address issues like class imbalance, data heterogeneity, and distributional shifts, often employing techniques such as linear programming, spectral graph analysis, and meta-learning. These advancements have significant implications for various fields, enhancing the accuracy and fairness of machine learning models in applications ranging from healthcare and neuroimaging to audio analysis and federated learning.