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We study the problem of PAC learning one-hidden-layer ReLU networks with $k$ hidden units on $\mathbb{R}^d$ under Gaussian marginals in the presence of additive label noise.
Orthogonal Polynomials
G. Szegö · 1967
Earlier work this paper cites.
A theory of the learnable
L. Valiant · 1984
Earlier work this paper cites.
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A. Blum, M. Furst, J. Jackson, M. Kearns, Y. Mansour, and S. Rudich · 1994
Earlier work this paper cites.
Efficient noise-tolerant learning from statistical queries
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Distribution-specific hardness of learning neural networks
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High-dimensional probability: An introduction with applications in data science
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Statistical algorithms and a lower bound for detecting planted cliques
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Superpolynomial lower bounds for learning one-layer neural networks using gradient descent, 2020
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Tight hardness results for learning depth-2 relu networks, 2020
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