Utilizing machine learning (ML)-based approaches for network intrusion detection systems
(NIDSs) raises valid concerns due to the inherent susceptibility of current ML models to …

The reliance of text classifiers on spurious correlations can lead to poor generalization at
deployment, raising concerns about their use in safety-critical domains such as healthcare …

Medical Image Analysis (MedIA) has become an essential tool in medicine and healthcare,
aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in …

Most existing methods that address out-of-distribution (OOD) generalization for node
classification on graphs primarily focus on a specific type of data biases, such as label …

Real-world data usually confronts severe class-imbalance problems, where several majority
classes have a significantly larger presence in the training set than minority classes. One …

Machine learning models are often trained to predict the outcome resulting from a human
decision. For example, if a doctor decides to test a patient for disease, will the patient test …

Vision Transformer (ViT) has emerged as a powerful architecture in the realm of modern
computer vision. However, its application in certain imaging fields, such as microscopy and …

In prediction tasks, there exist features that are related to the label in the same way across
different settings for that task; these are semantic features or semantics. Features with …

Abstract Machine learning models frequently experience performance drops under
distribution shifts. The underlying cause of such shifts may be multiple simultaneous factors …

With the rise of Machine Learning as a Service (MLaaS) platforms, safeguarding the
intellectual property of deep learning models is becoming paramount. Among various …