Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

In medical image analysis, we often need to build an image recognition system for a target
scenario with the access to small labeled data and abundant unlabeled data, as well as …

Human labeling is time-consuming and costly. This problem is further exacerbated in
extremely imbalanced class label scenarios, such as detecting fraudsters in online websites …

Collecting large-labeled data is an expensive and challenging issue for training deep neural
networks. To address this issue, active learning is recently studied where the active learner …

Tracking the spread of emerging pathogens is critical to the design of timely and effective
public health responses. Policymakers face the challenge of allocating finite resources for …

Node classification on graphs is an important task in many practical domains. It usually
requires labels for training, which can be difficult or expensive to obtain in practice. Given a …

The Internet provides great convenience for users to access, create, and share diverse
information and promotes the spread of misinformation. The cheap to produce, easily …