Learning Complexity

Learning complexity research investigates the computational resources required to train machine learning models effectively, focusing on minimizing resource demands while maintaining or improving performance. Current efforts concentrate on optimizing self-supervised learning, particularly by reducing model size and data volume, and on developing algorithms robust to strategic data manipulation or uncertainty in data structure. These advancements are crucial for making machine learning more accessible and efficient, impacting fields like computational pathology and improving the scalability of semi-supervised learning techniques. Ultimately, understanding and reducing learning complexity enables broader application of powerful machine learning tools across diverse scientific domains.