With the rise of big data, the quality of datasets has become a crucial factor affecting the
performance of machine learning models. High-quality datasets are essential for the …

Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …

Deep machine unlearning is the problem of'removing'from a trained neural network a subset
of its training set. This problem is very timely and has many applications, including the key …

Abstract Machine learning models are known to perpetuate and even amplify the biases
present in the data. However, these data biases frequently do not become apparent until …

Vision-language pre-training and instruction tuning have demonstrated general-purpose
capabilities in 2D visual reasoning tasks by aligning visual encoders with state-of-the-art …

Data-driven algorithms are studied and deployed in diverse domains to support critical
decisions, directly impacting people's well-being. As a result, a growing community of …

We explore the extent to which zero-shot vision-language models exhibit gender bias for
different vision tasks. Vision models traditionally required task-specific labels for …

Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

We explore the extent to which zero-shot vision-language models exhibit gender bias for
different vision tasks. Vision models traditionally required task-specific labels for …

Abstract Leveraging Large Language Models' remarkable proficiency in text-based tasks,
recent works on Multi-modal LLMs (MLLMs) extend them to other modalities like vision and …