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Over-parameterized deep neural networks trained by simple first-order methods are known to be able to fit any labeling of data.
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 1901
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On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Ruslan Salakhutdinov, and Ruosong Wang · 1904
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Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, and Keyulu Xu · 1905
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Rademacher and gaussian complexities: Risk bounds and structural results
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Frank Bauer, Sergei Pereverzev, and Lorenzo Rosasco · 2007
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Spectral algorithms for supervised learning
L Lo Gerfo, Lorenzo Rosasco, Francesca Odone, E De Vito, and Alessandro Verri · 2008
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A tail inequality for quadratic forms of subgaussian random vectors
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2012
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Early stopping and non-parametric regression: an optimal data-dependent stopping rule
Garvesh Raskutti, Martin J Wainwright, and Bin Yu · 2014
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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2014
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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2015
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Embracing error to enable rapid crowdsourcing
Ranjay A Krishna, Kenji Hata, Stephanie Chen, Joshua Kravitz, David A Shamma, Li Fei-Fei, and Michael S Bernstein · 2016
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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Deep learning is robust to massive label noise
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Learning to reweight examples for robust deep learning
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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Stochastic gradient descent optimizes over-parameterized deep ReLU networks
Difan Zou, Yuan Cao, Dongruo Zhou, and Quanquan Gu · 2018
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A generalization theory of gradient descent for learning over-parameterized deep relu networks
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Wide neural networks of any depth evolve as linear models under gradient descent
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How does disagreement help generalization against label corruption?
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