Simon Shaolei Du

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Jason D. LeeAssistant Professor of Electrical Engineering, Princeton UniversityVerified email at princeton.edu
Aarti SinghAssociate Professor of Machine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Ruosong WangPhD Student, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Wei HuUniversity of California, BerkeleyVerified email at berkeley.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at cs.princeton.edu
Zhiyuan LiGraduate Student, Princeton UniversityVerified email at princeton.edu
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAVerified email at ee.ucla.edu
Liwei WangProfessor, Peking UniversityVerified email at cis.pku.edu.cn
Sivaraman BalakrishnanCarnegie Mellon UniversityVerified email at stat.cmu.edu
Yuandong TianResearch Scientist, Meta/Facebook AI Research (FAIR)Verified email at fb.com
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Keyulu XuMITVerified email at mit.edu
Zhang ZihanTsinghua UniversityVerified email at mails.tsinghua.edu.cn
Yining WangAssistant professor, Warrington College of Business, University of FloridaVerified email at warrington.ufl.edu
Haochuan LiMITVerified email at mit.edu
Stefanie JegelkaMITVerified email at mit.edu
Ken-ichi KawarabayashiVerified email at nii.ac.jp

Simon Shaolei Du

Assistant Professor, School of Computer Science and Engineering, University of Washington

Verified email at cs.washington.edu - Homepage

Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Gradient descent finds global minima of deep neural networks SS Du, JD Lee, H Li, L Wang, X Zhai International Conference on Machine Learning 2019, 2018	714	2018
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks S Arora, SS Du, W Hu, Z Li, R Wang International Conference on Machine Learning 2019, 2019	543	2019
On exact computation with an infinitely wide neural net S Arora, SS Du, W Hu, Z Li, RR Salakhutdinov, R Wang Advances in Neural Information Processing Systems 32, 2019	481	2019
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh International Conference on Learning Representations 2019, 2018	434	2018
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima SS Du, JD Lee, Y Tian, B Poczos, A Singh International Conference on Machine Learning 2018, 2017	193	2017
On the power of over-parametrization in neural networks with quadratic activation SS Du, JD Lee International Conference on Machine Learning 2018, 2018	188	2018
Gradient descent can take exponential time to escape saddle points SS Du, C Jin, JD Lee, MI Jordan, A Singh, B Poczos Advances in neural information processing systems 30, 2017	186	2017
What Can Neural Networks Reason About? K Xu, J Li, M Zhang, SS Du, K Kawarabayashi, S Jegelka International Conference on Learning Representations 2020, 2019	128	2019
Stochastic variance reduction methods for policy evaluation SS Du, J Chen, L Li, L Xiao, D Zhou International Conference on Machine Learning 2017, 2017	128	2017
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels SS Du, K Hou, B Póczos, R Salakhutdinov, R Wang, K Xu Advances in Neural Information Processing Systems 2019, 2019	124	2019
When is a convolutional filter easy to learn? SS Du, JD Lee, Y Tian International Conference on Learning Representations 2018, 2017	115	2017
Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced SS Du, W Hu, JD Lee Advances in Neural Information Processing Systems 31, 2018	110	2018
Computationally efficient robust estimation of sparse functionals SS Du, S Balakrishnan, A Singh Conference on Learning Theory, 2017, 2017	110*	2017
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? SS Du, SM Kakade, R Wang, LF Yang International Conference on Learning Representation 2020, 2019	109	2019
Understanding the acceleration phenomenon via high-resolution differential equations B Shi, SS Du, MI Jordan, WJ Su Mathematical Programming, 1-70, 2021	108	2021
Provably efficient RL with rich observations via latent state decoding SS Du, A Krishnamurthy, N Jiang, A Agarwal, M Dudík, J Langford International Conference on Machine Learning 2019, 2019	104	2019
Harnessing the power of infinitely wide deep nets on small-data tasks S Arora, SS Du, Z Li, R Salakhutdinov, R Wang, D Yu International Conference on Learning Representations 2020, 2019	97	2019
How neural networks extrapolate: From feedforward to graph neural networks K Xu, M Zhang, J Li, SS Du, K Kawarabayashi, S Jegelka arXiv preprint arXiv:2009.11848, 2020	94	2020
Few-shot learning via learning the representation, provably SS Du, W Hu, SM Kakade, JD Lee, Q Lei arXiv preprint arXiv:2002.09434, 2020	90	2020
Linear convergence of the primal-dual gradient method for convex-concave saddle point problems without strong convexity SS Du, W Hu International Conference on Artificial Intelligence and Statistics 2019, 2018	89	2018

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