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2104.13628
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Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures
28 April 2021
Yuan Cao
Quanquan Gu
M. Belkin
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Papers citing
"Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures"
11 / 11 papers shown
Title
Directional Convergence, Benign Overfitting of Gradient Descent in leaky ReLU two-layer Neural Networks
Ichiro Hashimoto
MLT
65
0
0
22 May 2025
On the proliferation of support vectors in high dimensions
Daniel J. Hsu
Vidya Muthukumar
Ji Xu
63
45
0
22 Sep 2020
On the Optimal Weighted
ℓ
2
\ell_2
ℓ
2
Regularization in Overparameterized Linear Regression
Denny Wu
Ji Xu
70
122
0
10 Jun 2020
A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, a Precise Phase Transition, and the Corresponding Double Descent
Zhenyu Liao
Romain Couillet
Michael W. Mahoney
76
90
0
09 Jun 2020
Classification vs regression in overparameterized regimes: Does the loss function matter?
Vidya Muthukumar
Adhyyan Narang
Vignesh Subramanian
M. Belkin
Daniel J. Hsu
A. Sahai
86
151
0
16 May 2020
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Niladri S. Chatterji
Philip M. Long
43
108
0
25 Apr 2020
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks
Kaifeng Lyu
Jian Li
87
335
0
13 Jun 2019
Implicit Regularization in Deep Matrix Factorization
Sanjeev Arora
Nadav Cohen
Wei Hu
Yuping Luo
AI4CE
85
504
0
31 May 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
Trevor Hastie
Andrea Montanari
Saharon Rosset
Robert Tibshirani
188
743
0
19 Mar 2019
Reconciling modern machine learning practice and the bias-variance trade-off
M. Belkin
Daniel J. Hsu
Siyuan Ma
Soumik Mandal
232
1,650
0
28 Dec 2018
To understand deep learning we need to understand kernel learning
M. Belkin
Siyuan Ma
Soumik Mandal
60
419
0
05 Feb 2018
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