Simon Shaolei Du

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	总计	2019 年至今
引用	12244	11962
h 指数	50	50
i10 指数	94	93

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Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton University在 princeton.edu 的电子邮件经过验证
Ruosong WangAssistant Professor, Peking University在 pku.edu.cn 的电子邮件经过验证
Wei HuAssistant Professor of Computer Science and Engineering, University of Michigan在 umich.edu 的电子邮件经过验证
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon University在 cmu.edu 的电子邮件经过验证
Zhiyuan LiAssistant Professor, Toyota Technological Institute at Chicago在 ttic.edu 的电子邮件经过验证
Barnabas PoczosAssociate professor, Carnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
Sanjeev AroraProfessor of Computer Science, Princeton University在 cs.princeton.edu 的电子邮件经过验证
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMU在 cs.cmu.edu 的电子邮件经过验证
Liwei WangProfessor, Peking University在 cis.pku.edu.cn 的电子邮件经过验证
Xiyu Zhai在 mit.edu 的电子邮件经过验证
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLA在 ee.ucla.edu 的电子邮件经过验证
Yuandong TianResearch Scientist, Meta AI (FAIR)在 fb.com 的电子邮件经过验证
Sham M KakadeHarvard University在 seas.harvard.edu 的电子邮件经过验证
Zhang ZihanUniversity of Washington在 uw.edu 的电子邮件经过验证
Yining WangAssistant professor, Warrington College of Business, University of Florida在 warrington.ufl.edu 的电子邮件经过验证
Kevin JamiesonAssociate Professor, University of Washington在 cs.washington.edu 的电子邮件经过验证
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
Qiwen CuiPhD, University of Washington在 uw.edu 的电子邮件经过验证
Sivaraman BalakrishnanCarnegie Mellon University在 stat.cmu.edu 的电子邮件经过验证
Keyulu XuMIT在 mit.edu 的电子邮件经过验证

关注

Simon Shaolei Du

其他姓名Simon S. Du, Simon Du

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

在 cs.washington.edu 的电子邮件经过验证 - 首页

Machine Learning


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	1388	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	1089	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	1024	2019
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh International Conference on Learning Representations 2019, 2018	834	2018
How neural networks extrapolate: From feedforward to graph neural networks K Xu, M Zhang, J Li, SS Du, K Kawarabayashi, S Jegelka International Conference on Learning Representations, 2021	361	2021
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	307	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	301	2019
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	300	2019
On the power of over-parametrization in neural networks with quadratic activation SS Du, JD Lee International Conference on Machine Learning 2018, 2018	287	2018
Few-shot learning via learning the representation, provably SS Du, W Hu, SM Kakade, JD Lee, Q Lei International Conference on Learning Representations, 2021	282	2021
Understanding the acceleration phenomenon via high-resolution differential equations B Shi, SS Du, MI Jordan, WJ Su Mathematical Programming, 1-70, 2022	279	2022
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	279	2019
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	250	2019
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	249	2017
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	242	2021
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	237	2018
Stochastic variance reduction methods for policy evaluation SS Du, J Chen, L Li, L Xiao, D Zhou International Conference on Machine Learning 2017, 2017	217	2017
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	189	2019
Optimism in reinforcement learning with generalized linear function approximation Y Wang, R Wang, SS Du, A Krishnamurthy International Conference on Learning Representations, 2021	181	2021
Computationally efficient robust estimation of sparse functionals SS Du, S Balakrishnan, A Singh Conference on Learning Theory, 2017, 2017	162*	2017

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