Active Regression

Active regression focuses on efficiently learning regression models by selectively querying labels for only the most informative data points, minimizing the need for expensive or time-consuming labeling. Current research emphasizes developing algorithms that balance exploration (sampling diverse data) and exploitation (focusing on uncertain regions), often employing Bayesian hierarchical modeling or robust estimators to handle noisy or outlier-prone data. These advancements are improving the efficiency and robustness of regression models across various applications, from 3D object detection to mechanism design in auction theory, by significantly reducing the number of labels required for accurate predictions.