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• W2914852400 abstract "Aimed at explaining the surprisingly good generalization behavior of overparameterized deep networks, recent works have developed a variety of generalization bounds for deep learning, all based on the fundamental learning-theoretic technique of uniform convergence. While it is well-known that many of these existing bounds are numerically large, through numerous experiments, we bring to light a more concerning aspect of these bounds: in practice, these bounds can {em increase} with the training dataset size. Guided by our observations, we then present examples of overparameterized linear classifiers and neural networks trained by gradient descent (GD) where uniform convergence provably cannot ``explain generalization'' -- even if we take into account the implicit bias of GD {em to the fullest extent possible}. More precisely, even if we consider only the set of classifiers output by GD, which have test errors less than some small $epsilon$ in our settings, we show that applying (two-sided) uniform convergence on this set of classifiers will yield only a vacuous generalization guarantee larger than $1-epsilon$. Through these findings, we cast doubt on the power of uniform convergence-based generalization bounds to provide a complete picture of why overparameterized deep networks generalize well." @default.
• W2914852400 created "2019-02-21" @default.
• W2914852400 creator A5051270969 @default.
• W2914852400 creator A5075035644 @default.
• W2914852400 date "2019-02-13" @default.
• W2914852400 modified "2023-09-27" @default.
• W2914852400 title "Uniform convergence may be unable to explain generalization in deep learning" @default.
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• W2914852400 hasPublicationYear "2019" @default.
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