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Characterizing Implicit Bias in Terms of Optimization Geometry

22 February 2018

Papers citing "Characterizing Implicit Bias in Terms of Optimization Geometry"

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Title
Kernel and Rich Regimes in Overparametrized Models Blake E. Woodworth Suriya Gunasekar Pedro H. P. Savarese E. Moroshko Itay Golan Jason D. Lee Daniel Soudry Nathan Srebro 91 367 0 13 Jun 2019
Characterizing the implicit bias via a primal-dual analysis Ziwei Ji Matus Telgarsky 75 20 0 11 Jun 2019
Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization Navid Azizan Sahin Lale B. Hassibi 164 74 0 10 Jun 2019
The Implicit Bias of AdaGrad on Separable Data Qian Qian Xiaoyuan Qian 70 23 0 09 Jun 2019
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data Yan Li Ethan X. Fang Huan Xu T. Zhao 92 16 0 07 Jun 2019
PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization Thijs Vogels Sai Praneeth Karimireddy Martin Jaggi 105 322 0 31 May 2019
Implicit Regularization in Deep Matrix Factorization Sanjeev Arora Nadav Cohen Wei Hu Yuping Luo AI4CE 111 509 0 31 May 2019
Generalization bounds for deep convolutional neural networks Philip M. Long Hanie Sedghi MLT 136 90 0 29 May 2019
On Dropout and Nuclear Norm Regularization Poorya Mianjy R. Arora 143 23 0 28 May 2019
Convergence and Margin of Adversarial Training on Separable Data Zachary B. Charles Shashank Rajput S. Wright Dimitris Papailiopoulos AAML 71 17 0 22 May 2019
A type of generalization error induced by initialization in deep neural networks Yaoyu Zhang Zhi-Qin John Xu Yaoyu Zhang Zheng Ma 128 51 0 19 May 2019
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models Mor Shpigel Nacson Suriya Gunasekar Jason D. Lee Nathan Srebro Daniel Soudry 95 94 0 17 May 2019
Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation Colin Wei Tengyu Ma 87 110 0 09 May 2019
A Selective Overview of Deep Learning Jianqing Fan Cong Ma Yiqiao Zhong BDL VLM 206 135 0 10 Apr 2019
A Stochastic Interpretation of Stochastic Mirror Descent: Risk-Sensitive Optimality Navid Azizan B. Hassibi 28 5 0 03 Apr 2019
High-Dimensional Linear Regression via Implicit Regularization P. Zhao Yun Yang Qiaochu He 71 26 0 22 Mar 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 302 747 0 19 Mar 2019
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise Yeming Wen Kevin Luk Maxime Gazeau Guodong Zhang Harris Chan Jimmy Ba ODL 73 22 0 21 Feb 2019
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks Yuan Cao Quanquan Gu ODL MLT AI4CE 153 158 0 04 Feb 2019
Asymmetric Valleys: Beyond Sharp and Flat Local Minima Haowei He Gao Huang Yang Yuan ODL MLT 86 150 0 02 Feb 2019
Error Feedback Fixes SignSGD and other Gradient Compression Schemes Sai Praneeth Karimireddy Quentin Rebjock Sebastian U. Stich Martin Jaggi 113 503 0 28 Jan 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 234 974 0 24 Jan 2019
Improving Generalization of Deep Neural Networks by Leveraging Margin Distribution Shen-Huan Lyu Lu Wang Zhi Zhou 43 11 0 27 Dec 2018
Deep Geometric Prior for Surface Reconstruction Francis Williams T. Schneider Claudio Silva Denis Zorin Joan Bruna Daniele Panozzo 3DPC 130 191 0 27 Nov 2018
Minimum weight norm models do not always generalize well for over-parameterized problems Vatsal Shah Anastasios Kyrillidis Sujay Sanghavi 105 21 0 16 Nov 2018
A Continuous-Time View of Early Stopping for Least Squares Alnur Ali J. Zico Kolter Robert Tibshirani 102 97 0 23 Oct 2018
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods R. Freund Paul Grigas Rahul Mazumder 58 10 0 20 Oct 2018
A Modern Take on the Bias-Variance Tradeoff in Neural Networks Brady Neal Sarthak Mittal A. Baratin Vinayak Tantia Matthew Scicluna Simon Lacoste-Julien Ioannis Mitliagkas 87 168 0 19 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei Jason D. Lee Qiang Liu Tengyu Ma 268 245 0 12 Oct 2018
Diffusion Scattering Transforms on Graphs Fernando Gama Alejandro Ribeiro Joan Bruna GNN 97 103 0 22 Jun 2018
When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models? Tengyu Xu Yi Zhou Kaiyi Ji Yingbin Liang 90 19 0 12 Jun 2018
Implicit regularization and solution uniqueness in over-parameterized matrix sensing Kelly Geyer Anastasios Kyrillidis A. Kalev 116 4 0 06 Jun 2018
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate Mor Shpigel Nacson Nathan Srebro Daniel Soudry FedML MLT 102 102 0 05 Jun 2018
Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization Navid Azizan B. Hassibi 86 64 0 04 Jun 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro MDE 139 414 0 01 Jun 2018
Risk and parameter convergence of logistic regression Ziwei Ji Matus Telgarsky 86 130 0 20 Mar 2018
Convergence of Gradient Descent on Separable Data Mor Shpigel Nacson Jason D. Lee Suriya Gunasekar Pedro H. P. Savarese Nathan Srebro Daniel Soudry 109 169 0 05 Mar 2018
Spurious Valleys in Two-layer Neural Network Optimization Landscapes Luca Venturi Afonso S. Bandeira Joan Bruna 97 74 0 18 Feb 2018
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 241 925 0 27 Oct 2017
Nonparametric regression using deep neural networks with ReLU activation function Johannes Schmidt-Hieber 244 817 0 22 Aug 2017