Computerized adaptive testing (CAT), as a tool that can efficiently measure student's ability,
has been widely used in various standardized tests (eg, GMAT and GRE). The adaptivity of …

Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …

Imbalanced data always has a serious impact on a predictive model, and most under-
sampling techniques consume more time and suffer from loss of samples containing critical …

Central to active learning (AL) is what data should be selected for annotation. Existing works
attempt to select highly uncertain or informative data for annotation. Nevertheless, it remains …

Active Learning has been a popular method to circumvent the labeling cost in machine
learning methods. The majority of active learning approaches can be classified into two …

Abstract 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 …

Transfer learning (TL) has been widely used in electroencephalogram (EEG)-based brain-
computer interfaces (BCIs) for reducing calibration efforts. However, backdoor attacks could …

Due to the lack of the labeled data and the complex structures of various data, it is very hard
to learn the uncertainty and representativeness accurately in active learning. In this paper …

Meta-learning algorithms for active learning are emerging as a promising paradigm for
learning the" best" active learning strategy. However, current learning-based active learning …

Computerized adaptive testing (CAT) targets to accurately assess the student's proficiency in
the required subject/area. The key issue is how to design a question selector that adaptively …