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Risk Bounds for High-dimensional Ridge Function Combinations Including Neural Networks
5 July 2016
Jason M. Klusowski
Andrew R. Barron
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Papers citing
"Risk Bounds for High-dimensional Ridge Function Combinations Including Neural Networks"
12 / 12 papers shown
Title
Universal approximation results for neural networks with non-polynomial activation function over non-compact domains
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Learning Combinations of Sigmoids Through Gradient Estimation
Stratis Ioannidis
Andrea Montanari
30
2
0
22 Aug 2017
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong
Zhao Song
Prateek Jain
Peter L. Bartlett
Inderjit S. Dhillon
MLT
175
337
0
10 Jun 2017
Minimax Lower Bounds for Ridge Combinations Including Neural Nets
Jason M. Klusowski
Andrew R. Barron
77
22
0
09 Feb 2017
Learning Halfspaces and Neural Networks with Random Initialization
Yuchen Zhang
Jason D. Lee
Martin J. Wainwright
Michael I. Jordan
54
36
0
25 Nov 2015
ℓ
1
\ell_1
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1
-regularized Neural Networks are Improperly Learnable in Polynomial Time
Yuchen Zhang
Jason D. Lee
Michael I. Jordan
186
103
0
13 Oct 2015
Learning by Transduction
A. Gammerman
V. Vovk
V. Vapnik
186
515
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30 Jan 2013
Tensor decompositions for learning latent variable models
Anima Anandkumar
Rong Ge
Daniel J. Hsu
Sham Kakade
Matus Telgarsky
440
1,145
0
29 Oct 2012
Minimax rates of estimation for high-dimensional linear regression over
ℓ
q
\ell_q
ℓ
q
-balls
Garvesh Raskutti
Martin J. Wainwright
Bin Yu
220
575
0
11 Oct 2009
Some sharp performance bounds for least squares regression with
L
1
L_1
L
1
regularization
Tong Zhang
136
270
0
20 Aug 2009
Approximation and learning by greedy algorithms
Andrew R. Barron
A. Cohen
W. Dahmen
Ronald A. DeVore
335
323
0
12 Mar 2008
Aggregation for Gaussian regression
F. Bunea
Alexandre B. Tsybakov
M. Wegkamp
507
362
0
19 Oct 2007
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