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We investigate the properties of random feature ridge regression (RFRR) given by a two-layer neural network with random Gaussian initialization.
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Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, and Amir Zandieh · 2017
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Francis Bach · 2017
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Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
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Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2018
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Zhenyu Liao and Romain Couillet · 2018
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Alnur Ali, J Zico Kolter, and Ryan J Tibshirani · 2019
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Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2019
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Zhenyu Liao and Romain Couillet · 2019
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Sifan Liu and Edgar Dobriban · 2019
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The generalization error of random features regression: Precise asymptotics and the double descent curve
Song Mei and Andrea Montanari · 2019
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Consistent risk estimation in moderately high-dimensional linear regression
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High-dimensional asymptotics of feature learning: How one gradient step improves the representation
Jimmy Ba, Murat A Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, and Greg Yang · 2022
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Ben Adlam and Jeffrey Pennington · 2020
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Oussama Dhifallah and Yue M Lu · 2020
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Spectra of the conjugate kernel and neural tangent kernel for linear-width neural networks
Zhou Fan and Zhichao Wang · 2020
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Generalisation error in learning with random features and the hidden manifold model
Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, and Lenka Zdeborová · 2020
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Sebastian Goldt, Marc Mézard, Florent Krzakala, and Lenka Zdeborová · 2020
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The surprising simplicity of the early-time learning dynamics of neural networks
Wei Hu, Lechao Xiao, Ben Adlam, and Jeffrey Pennington · 2020
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Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clément Hongler, and Franck Gabriel · 2020
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Sharp asymptotics of kernel ridge regression beyond the linear regime
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Universality laws for high-dimensional learning with random features
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Estimating functionals of the out-of-sample error distribution in high-dimensional ridge regression
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