Semantic communication is viewed as a revolutionary paradigm that can potentially
transform how we design and operate wireless communication systems. However, despite a …

This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

The impressive performance of recent language models across a wide range of tasks
suggests that they possess a degree of abstract reasoning skills. Are these skills general …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Regularization is a fundamental technique to prevent over-fitting and to improve
generalization performances by constraining a model's complexity. Current Deep Networks …

As NLP models achieved state-of-the-art performances over benchmarks and gained wide
applications, it has been increasingly important to ensure the safe deployment of these …

Machine learning systems are known to be sensitive to spurious correlations between non-
essential features of the inputs (eg, background, texture, and secondary objects) and the …

Abstract Knowledge tracing is a crucial component of intelligent educational systems and
deep learning technologies have significantly propelled its advancement. However, most …

We study the membership inference (MI) attack against classifiers, where the attacker's goal
is to determine whether a data instance was used for training the classifier. Through …

The termspurious correlations' has been used in NLP to informally denote any undesirable
feature-label correlations. However, a correlation can be undesirable because (i) the feature …