Evaluating fairness can be challenging in practice because the sensitive attributes of data
are often inaccessible due to privacy constraints. The go-to approach that the industry …

Although data-driven fault diagnosis methods have achieved remarkable results, these
achievements often rely on high-quality datasets without noisy labels, which can mislead the …

Learning from crowds describes that the annotations of training data are obtained with
crowd-sourcing services. Multiple annotators each complete their own small part of the …

Designing robust loss functions is popular in learning with noisy labels while existing
designs did not explicitly consider the overfitting property of deep neural networks (DNNs) …

LLM hallucination, ie generating factually incorrect yet seemingly convincing answers, is
currently a major threat to the trustworthiness and reliability of LLMs. The first step towards …

Federated Learning (FL) heavily depends on label quality for its performance. However, the
label distribution among individual clients is always both noisy and heterogeneous. The …

In film gender studies the concept of" male gaze" refers to the way the characters are
portrayed on-screen as objects of desire rather than subjects. In this article we introduce a …