Active learning (AL) combines data labeling and model training to minimize the labeling cost
by prioritizing the selection of high value data that can best improve model performance. In …

Federated learning enables multiple decentralized clients to learn collaboratively without
sharing local data. However, the expensive annotation cost on local clients remains an …

The goal of the paper is to design active learning strategies which lead to domain adaptation
under an assumption of Lipschitz functions. Building on previous work by Mansour et …

Deep learning on large-scale data is currently dominant nowadays. The unprecedented
scale of data has been arguably one of the most important driving forces behind its success …

Student learners typically engage in an iterative process of actively updating its hypotheses,
like active learning. While this behavior can be advantageous, there is an inherent risk of …

Deep neural networks have great representation power, but typically require large numbers
of training examples. This motivates deep active learning methods that can significantly …

Federated affective computing, which deploys traditional affective computing in a distributed
framework, achieves a trade-off between privacy and utility, and offers a wide variety of …

We study online active learning for classifying streaming instances within the framework of
statistical learning theory. At each time, the learner either queries the label of the current …

We derive a novel active learning algorithm in the streaming setting for binary classification
tasks. The algorithm leverages weak labels to minimize the number of label requests, and …

Active learning (AL) aims to maximize the learning performance of the current hypothesis by
drawing as few labels as possible from an input distribution. Generally, most existing AL …