Risk Bounds for Over-parameterized Maximum Margin Classification on
Sub-Gaussian Mixtures

Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures

28 April 2021

Quanquan Gu

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 $\ell_2$ Regularization in Overparameterized Linear Regression$ On the Optimal Weighted $\ell_2$ 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 81 151 0 16 May 2020
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime Niladri S. Chatterji Philip M. Long 41 108 0 25 Apr 2020
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 84 335 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 227 1,647 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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