As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning
varies depending on context and community. Researchers either focus on narrow technical …

Fairness and robustness are critical elements of Trustworthy AI that need to be addressed
together. Fairness is about learning an unbiased model while robustness is about learning …

There is a fast-growing literature in addressing the fairness of AI models (fair-AI), with a
continuous stream of new conceptual frameworks, methods, and tools. How much can we …

Graph convolutional neural networks (GNNs) have an excellent expression ability for
complex systems. However, the smoothing hypothesis based GNNs have certain limitations …

The digitization of healthcare data coupled with advances in computational capabilities has
propelled the adoption of machine learning (ML) in healthcare. However, these methods can …

Robustness is becoming another important challenge of federated learning in that the data
collection process in each client is naturally accompanied by noisy labels. However, it is far …