Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate

5 June 2018

Papers citing "Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate"

22 / 22 papers shown

Title
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks Chenyang Zhang Peifeng Gao Difan Zou Yuan Cao OOD MLT 59 0 0 11 Apr 2025
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks Yuan Cao Difan Zou Yuan-Fang Li Quanquan Gu MLT 37 5 0 20 Jun 2023
Implicit Bias of Gradient Descent for Logistic Regression at the Edge of Stability Jingfeng Wu Vladimir Braverman Jason D. Lee 29 17 0 19 May 2023
Deep Linear Networks can Benignly Overfit when Shallow Ones Do Niladri S. Chatterji Philip M. Long 23 8 0 19 Sep 2022
On the Implicit Bias in Deep-Learning Algorithms Gal Vardi FedML AI4CE 34 72 0 26 Aug 2022
Why adversarial training can hurt robust accuracy Jacob Clarysse Julia Hörrmann Fanny Yang AAML 13 18 0 03 Mar 2022
Robust Training under Label Noise by Over-parameterization Sheng Liu Zhihui Zhu Qing Qu Chong You NoLa OOD 27 106 0 28 Feb 2022
Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond Matan Schliserman Tomer Koren 24 23 0 27 Feb 2022
Benign Overfitting in Adversarially Robust Linear Classification Jinghui Chen Yuan Cao Quanquan Gu AAML SILM 34 10 0 31 Dec 2021
On the Power of Differentiable Learning versus PAC and SQ Learning Emmanuel Abbe Pritish Kamath Eran Malach Colin Sandon Nathan Srebro MLT 77 23 0 09 Aug 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features Zhihui Zhu Tianyu Ding Jinxin Zhou Xiao Li Chong You Jeremias Sulam Qing Qu 24 194 0 06 May 2021
Label-Imbalanced and Group-Sensitive Classification under Overparameterization Ganesh Ramachandra Kini Orestis Paraskevas Samet Oymak Christos Thrampoulidis 27 93 0 02 Mar 2021
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks Bohan Wang Qi Meng Wei Chen Tie-Yan Liu 24 33 0 11 Dec 2020
Exploring Weight Importance and Hessian Bias in Model Pruning Mingchen Li Yahya Sattar Christos Thrampoulidis Samet Oymak 28 3 0 19 Jun 2020
To Each Optimizer a Norm, To Each Norm its Generalization Sharan Vaswani Reza Babanezhad Jose Gallego Aaron Mishkin Simon Lacoste-Julien Nicolas Le Roux 26 8 0 11 Jun 2020
Towards Understanding the Spectral Bias of Deep Learning Yuan Cao Zhiying Fang Yue Wu Ding-Xuan Zhou Quanquan Gu 32 214 0 03 Dec 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 43 321 0 13 Jun 2019
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models Mor Shpigel Nacson Suriya Gunasekar J. Lee Nathan Srebro Daniel Soudry 27 91 0 17 May 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 24 446 0 21 Nov 2018
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods R. Freund Paul Grigas Rahul Mazumder 20 10 0 20 Oct 2018
Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization G. Wang G. Giannakis Jie Chen MLT 24 131 0 14 Aug 2018
When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models? Tengyu Xu Yi Zhou Kaiyi Ji Yingbin Liang 29 19 0 12 Jun 2018