Selective Learning

Selective learning focuses on improving machine learning models by strategically choosing which data points to learn from, aiming to enhance robustness, generalization, and efficiency. Current research emphasizes developing algorithms that identify and prioritize informative data, mitigating the negative effects of noise and outliers, and exploring techniques like dynamic regularization and adversarial perturbation to achieve better calibration and out-of-distribution generalization. This approach holds significant promise for improving the performance and reliability of various machine learning applications, particularly in resource-constrained environments or domains with noisy data, such as healthcare and finance.