Sample efficiency is critical in environments where data collection is expensive or limited, such as robotics or medical diagnostics. It measures how well a learning algorithm can generalize from limited data, which is especially important in reinforcement learning and supervised learning contexts. High sample efficiency implies that a model can learn effective policies or make accurate predictions without needing vast amounts of data, thus saving resources and time. Techniques to improve sample efficiency include transfer learning, where a model trained on one task is adapted to another related task, and meta-learning, which involves designing models that learn to learn across multiple tasks efficiently.
Historical Overview: The concept of sample efficiency has been integral to statistical learning theory since its early days but became particularly prominent in the context of machine learning in the early 2000s. As computational power increased, allowing for models that could process larger datasets, the focus shifted towards developing algorithms that could perform well even with fewer data points.
Key Contributors: No single figure or group can be credited exclusively for developments in sample efficiency, as it has been a focal point of research across many sub-disciplines of machine learning. However, researchers like Geoffrey Hinton and Yann LeCun have significantly contributed to the broader field of efficient learning algorithms through their work in deep learning and neural networks. In reinforcement learning, Richard S. Sutton’s work on learning with limited feedback has been foundational.