Based on our theoretical analysis, we propose an active learning algorithm that employs regret minimization to minimize the FIR.

V arious dedicated algorithms have been recently proposed and studied by the machine-learning community. In contrast, active bipartite ranking rule is poorly documented in the literature. Due to its global nature, a strategy for labeling sequentially data points that are difficult to rank w.r.t. to the others is

TBAL is emerging as a widely-used solution in practice.

When the loss is set as squared loss, we can modify the functions regarding the truncated spectral projection.

Graph Neural Networks (GNNs) are crucial for machine learning applications with graph-structured data, but their success depends on sufficient labeled data. We present a novel active learning (AL) method for GNNs, extending the Expected Model Change Maximization (EMCM) principle to improve prediction performance on unlabeled data. By presenting a Bayesian interpretation for the node embeddings generated by GNNs under the semi-supervised setting, we efficiently compute the closed-form EMCM acquisition function as the selection criterion for AL without re-training.

In active learning (AL), we focus on reducing the data annotation cost from the model training perspective. However, "testing", which often refers to the model