Sample Efficient

Sample-efficient learning aims to achieve high performance with minimal data, addressing the limitations of data scarcity and high computational costs in various fields. Current research focuses on developing algorithms and model architectures, such as Bayesian optimization, reinforcement learning (including model-based and model-free approaches), and diffusion models, that effectively leverage limited data through techniques like transfer learning, active learning, and data augmentation. This pursuit is crucial for advancing applications in robotics, materials science, clinical trials, and other domains where data acquisition is expensive or difficult, enabling faster progress and more efficient resource utilization.