Batch Selection

Batch selection focuses on strategically choosing subsets of data for machine learning tasks, aiming to optimize training efficiency and model performance. Current research emphasizes developing algorithms that select diverse, representative batches, often prioritizing "hard" samples (e.g., those with high loss or associated with minority classes) or leveraging Bayesian optimization and bandit algorithms to guide the selection process. These advancements are significant because they can drastically reduce training time and computational costs for large datasets, improving the scalability and practicality of machine learning models across various applications.