Teaching Dimension

The teaching dimension quantifies the minimum number of examples needed to uniquely identify a concept within a concept class, a crucial problem in machine teaching. Current research focuses on developing efficient algorithms for determining this dimension, exploring various model architectures like non-clashing teaching maps and investigating the computational complexity of finding optimal teaching sets, particularly for specific concept classes such as balls in graphs. These advancements have implications for improving the efficiency of machine learning algorithms and reducing the annotation burden in applications like medical image analysis, where labeled data is scarce.