Iterative Machine Teaching

Iterative machine teaching (IMT) focuses on efficiently training machine learning models by strategically providing training examples one at a time, adapting to the learner's progress. Current research explores extending IMT beyond parameterized models to handle more general functions, and improving robustness by focusing on adversarial examples and data augmentation techniques. These advancements aim to reduce the number of training examples needed and enhance the resulting model's accuracy and resilience, impacting both theoretical understanding of learning and practical applications requiring efficient and robust model training.