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 …

Robustness has been extensively studied in reinforcement learning (RL) to handle various
forms of uncertainty such as random perturbations, rare events, and malicious attacks. In this …

Ensuring both transparency and safety is critical when deploying Deep Neural Networks
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to
large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate …

Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

Abstract Concept Bottleneck Models (CBMs) map the black-box visual representations
extracted by deep neural networks onto a set of interpretable concepts and use the concepts …

Novelty detection is a fundamental task of machine learning which aims to detect abnormal
(ie out-of-distribution (OOD)) samples. Since diffusion models have recently emerged as the …

Machine learning has demonstrated remarkable performance over finite datasets, yet
whether the scores over the fixed benchmarks can sufficiently indicate the model's …

Spurious bias, a tendency to use spurious correlations between non-essential input
attributes and target variables for predictions, has revealed a severe robustness pitfall in …

Abstract While standard Empirical Risk Minimization (ERM) training is proven effective for
image classification on in-distribution data it fails to perform well on out-of-distribution …