Subset Selection

Subset selection focuses on identifying optimal subsets of data for various machine learning tasks, aiming to improve efficiency, accuracy, and robustness while mitigating issues like high computational costs and noisy labels. Current research emphasizes developing efficient algorithms, including greedy heuristics, attention-based neural networks, and Pareto optimization approaches, to select these subsets, often incorporating considerations of diversity, fairness, and model-specific characteristics. This field is crucial for advancing machine learning by enabling the training of high-performing models on smaller, more manageable datasets, and improving the interpretability and trustworthiness of AI systems across diverse applications.