Task Sampling

Task sampling, the strategic selection of training tasks in machine learning, aims to improve model generalization and efficiency by focusing learning on the most informative examples. Current research emphasizes developing task-specific sampling strategies, often integrated with meta-learning frameworks or reinforcement learning algorithms, to address performance imbalances across diverse tasks and improve sample efficiency. This work is significant because effective task sampling can lead to more robust and generalizable models, impacting various applications from medical imaging to few-shot learning and robotics, by reducing training costs and improving performance on challenging, real-world problems.