Fetching the paper…
Reading the bibliography…
We study the generalization properties of minimum-norm solutions for three over-parametrized machine learning models including the random feature model, the two-layer neural network model and the residual network model.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 1912
Earlier work this paper cites.
Theory of reproducing kernels
Nachman Aronszajn · 1950
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
Andrew R. Barron · 1993
Earlier work this paper cites.
Optimal rates for the regularized least-squares algorithm
Andrea Caponnetto and Ernesto De Vito · 2007
Earlier work this paper cites.
High-dimensional integration: the quasi-Monte Carlo way
Josef Dick, Frances Y. Kuo and Ian H. Sloan · 2013
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
Earlier work this paper cites.
Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate
Mikhail Belkin, Daniel J Hsu, and Partha Mitra · 2018
Cited alongside, same era.
To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, and Soumik Mandal · 2018
Cited alongside, same era.
A priori estimates of the population risk for two-layer neural networks
Weinan E, Chao Ma, and Lei Wu · 2018
Cited alongside, same era.
Just interpolate: Kernel “ridgeless” regression can generalize
Tengyuan Liang and Alexander Rakhlin · 2018
Cited alongside, same era.
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2019
Cited alongside, same era.
Barron spaces and the flow-induced function spaces for neural network models
Weinan E, Chao Ma, and Lei Wu · 2019
Closest in time.
Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
Closest in time.
On the risk of minimum-norm interpolants and restricted lower isometry of kernels
Tengyuan Liang, Alexander Rakhlin, and Xiyu Zhai · 2019
Closest in time.
Consistency of interpolation with laplace kernels is a high-dimensional phenomenon
Alexander Rakhlin and Xiyu Zhai · 2019
Closest in time.
Benign overfitting in linear regression
Peter L Bartlett, Philip M Long, Gábor Lugosi, and Alexander Tsigler · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A priori estimates of the population risk for residual networks
Weinan E, Chao Ma, and Qingcan Wang · 2019
Cited alongside, same era.