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[r/ML] [R] From Garbage to Gold: A Formal Proof that GIGO Fails for High-Dimensional Data with Latent Structure — with a Connection to Benign Overfitting Prerequisites

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Summary

A new paper formally proves that the "Garbage In, Garbage Out" principle fails for high-dimensional data exhibiting a latent hierarchical structure. It demonstrates that a breadth strategy for expanding predictor sets asymptotically dominates a depth strategy in these cases. This theoretical finding offers new insights into how machine learning models can extract valuable information from noisy data, with implications for understanding benign overfitting.

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