Simon Shaolei Du

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Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Ruosong WangPhD Student, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganVerified email at umich.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityVerified email at cs.princeton.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoVerified email at ttic.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Liwei WangProfessor, Peking UniversityVerified email at cis.pku.edu.cn
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAVerified email at ee.ucla.edu
Xiyu ZhaiVerified email at mit.edu
Yuandong TianResearch Scientist, Meta AI (FAIR)Verified email at fb.com
Yining WangAssistant professor, Warrington College of Business, University of FloridaVerified email at warrington.ufl.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityVerified email at stat.cmu.edu
Zhang ZihanTsinghua UniversityVerified email at mails.tsinghua.edu.cn
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
Kevin JamiesonAssistant Professor, University of WashingtonVerified email at cs.washington.edu
Yifang ChenUniversity of WashingtonVerified email at cs.washington.edu

Simon Shaolei Du

Other namesSimon S. Du, Simon 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	1090	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	835	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	764	2019
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh International Conference on Learning Representations 2019, 2018	645	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	257	2017
On the power of over-parametrization in neural networks with quadratic activation SS Du, JD Lee International Conference on Machine Learning 2018, 2018	247	2018
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	237	2020
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	223	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	214	2019
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	210	2019
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	206	2019
Understanding the acceleration phenomenon via high-resolution differential equations B Shi, SS Du, MI Jordan, WJ Su Mathematical Programming, 1-70, 2021	194	2021
Few-shot learning via learning the representation, provably SS Du, W Hu, SM Kakade, JD Lee, Q Lei arXiv preprint arXiv:2002.09434, 2020	188	2020
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	185	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	176	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	171	2018
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	152	2019
Bilinear classes: A structural framework for provable generalization in rl S Du, S Kakade, J Lee, S Lovett, G Mahajan, W Sun, R Wang International Conference on Machine Learning, 2826-2836, 2021	151	2021
Computationally efficient robust estimation of sparse functionals SS Du, S Balakrishnan, A Singh Conference on Learning Theory, 2017, 2017	141*	2017
Optimism in reinforcement learning with generalized linear function approximation Y Wang, R Wang, SS Du, A Krishnamurthy arXiv preprint arXiv:1912.04136, 2019	130	2019

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