Sample Diversity

Sample diversity in machine learning focuses on selecting training data subsets that are both representative of the overall distribution and maximally diverse, improving model generalization and robustness. Current research emphasizes developing novel sampling algorithms, often incorporating techniques like k-means++, determinantal point processes, and modified Frank-Wolfe algorithms, to achieve this balance within various model architectures, including diffusion models and large language models. This work is crucial for addressing challenges like domain adaptation, semi-supervised learning, and mitigating catastrophic forgetting in continual learning, ultimately leading to more efficient and reliable machine learning systems across diverse applications.