Provable Generalization of SGD-trained Neural Networks of Any Width in
the Presence of Adversarial Label Noise

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise

4 January 2021

Quanquan Gu

Papers citing "Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise"

19 / 19 papers shown

Title
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel Stanislav Fort Gintare Karolina Dziugaite Mansheej Paul Sepideh Kharaghani Daniel M. Roy Surya Ganguli 97 190 0 28 Oct 2020
Noise in Classification Maria-Florina Balcan Nika Haghtalab 26 11 0 10 Oct 2020
Learning Over-Parametrized Two-Layer ReLU Neural Networks beyond NTK Yuanzhi Li Tengyu Ma Hongyang R. Zhang MLT 41 27 0 09 Jul 2020
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks Ilias Diakonikolas D. Kane Vasilis Kontonis Nikos Zarifis 51 65 0 22 Jun 2020
The Pitfalls of Simplicity Bias in Neural Networks Harshay Shah Kaustav Tamuly Aditi Raghunathan Prateek Jain Praneeth Netrapalli AAML 65 359 0 13 Jun 2020
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise Ilias Diakonikolas Vasilis Kontonis Christos Tzamos Nikos Zarifis 37 28 0 11 Jun 2020
Directional convergence and alignment in deep learning Ziwei Ji Matus Telgarsky 54 171 0 11 Jun 2020
Learning Halfspaces with Massart Noise Under Structured Distributions Ilias Diakonikolas Vasilis Kontonis Christos Tzamos Nikos Zarifis 44 61 0 13 Feb 2020
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? Zixiang Chen Yuan Cao Difan Zou Quanquan Gu 59 122 0 27 Nov 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 79 335 0 13 Jun 2019
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 75 363 0 13 Jun 2019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao Quanquan Gu MLT AI4CE 80 389 0 30 May 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu Yuanzhi Li 388 183 0 24 May 2019
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks Yuan Cao Quanquan Gu ODL MLT AI4CE 76 156 0 04 Feb 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 178 448 0 21 Nov 2018
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei Jason D. Lee Qiang Liu Tengyu Ma 193 244 0 12 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 214 1,272 0 04 Oct 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 304 12,063 0 19 Jun 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 336 4,626 0 10 Nov 2016