Machine learning models, while progressively advanced, rely heavily on the IID assumption,
which is often unfulfilled in practice due to inevitable distribution shifts. This renders them …

The conventional evaluation protocols on machine learning models rely heavily on a
labeled, iid-assumed testing dataset, which is not often present in real world applications …

This article presents a comprehensive survey on test optimization in deep neural network
(DNN) testing. Here, test optimization refers to testing with low data labeling effort. We …

Accurately estimating model performance poses a significant challenge, particularly in
scenarios where the source and target domains follow different data distributions. Most …

Real-time data may undergo distribution drift due to changes in operating conditions and
other factors, which can affect the classification accuracy of online fault diagnosis models …

Analyzing model performance in various unseen environments is a critical research problem
in the machine learning community. To study this problem, it is important to construct a …

Performance evaluation plays a crucial role in the development life cycle of large language
models (LLMs). It estimates the model's capability, elucidates behavior characteristics, and …

Deep learning models often rely only on a small set of features even when there is a rich set
of predictive signals in the training data. This makes models brittle and sensitive to …

Convolutional neural networks (CNNs) have achieved promising results in many tasks, and
evaluating the model's generalization ability based on the trained model and training data is …

Domain adaptation in fault diagnosis can efficiently handle different data distributions by co-
training source and target domain data. However, the source domain data may not be …