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In recent years we see a rapidly growing line of research which shows learnability of various models via common neural network algorithms.
Samet Oymak and Mahdi Soltanolkotabi · 1902
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Learning polynomials with neural networks
A. Andoni, R. Panigrahy, G. Valiant, and L. Zhang · 2014
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Toward deeper understanding of neural networks: The power of initialization and a dual view on expressivity
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Sgd learns over-parameterized networks that provably generalize on linearly separable data
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Sgd learns the conjugate kernel class of the network
Amit Daniely · 2017
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Learning and generalization in overparameterized neural networks, going beyond two layers
Zeyuan Allen-Zhu, Yuanzhi Li, and Yingyu Liang · 2018
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Rong Ge, Runzhe Wang, and Haoyu Zhao · 2019
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Limitations of lazy training of two-layers neural network
Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, and Andrea Montanari · 2019
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Ziwei Ji and Matus Telgarsky · 2019
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Wide neural networks of any depth evolve as linear models under gradient descent
Jaehoon Lee, Lechao Xiao, Samuel S Schoenholz, Yasaman Bahri, Jascha Sohl-Dickstein, and Jeffrey Pennington · 2019
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Generalization bounds of stochastic gradient descent for wide and deep neural networks
Yuan Cao and Quanquan Gu · 2019
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Neural networks learning and memorization with (almost) no over-parameterization
Amit Daniely · 2019
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Jonathan Fiat, Eran Malach, and Shai Shalev-Shwartz · 2019
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On the power and limitations of random features for understanding neural networks
Gilad Yehudai and Ohad Shamir · 2019
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An improved analysis of training over-parameterized deep neural networks
Difan Zou and Quanquan Gu · 2019
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