Simon Shaolei Du

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	すべて	2019 年以来
引用	11604	11323
h 指標	48	48
i10 指標	91	90

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60 件の論文

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共著者

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

フォロー

Simon Shaolei Du

その他の名前Simon S. Du, Simon Du

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

確認したメールアドレス: cs.washington.edu - ホームページ

Machine Learning


タイトル引用回数順公開年順タイトル順	引用先引用先	年
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	1332	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	1039	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	976	2019
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh International Conference on Learning Representations 2019, 2018	806	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	342	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	295	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	291	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	280	2019
On the power of over-parametrization in neural networks with quadratic activation SS Du, JD Lee International Conference on Machine Learning 2018, 2018	280	2018
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	269	2019
Few-shot learning via learning the representation, provably SS Du, W Hu, SM Kakade, JD Lee, Q Lei International Conference on Learning Representations, 2021	267	2021
Understanding the acceleration phenomenon via high-resolution differential equations B Shi, SS Du, MI Jordan, WJ Su Mathematical Programming, 1-70, 2022	261	2022
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	247	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	241	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	229	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	224	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	203	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	181	2019
Optimism in reinforcement learning with generalized linear function approximation Y Wang, R Wang, SS Du, A Krishnamurthy International Conference on Learning Representations, 2021	172	2021
Computationally efficient robust estimation of sparse functionals SS Du, S Balakrishnan, A Singh Conference on Learning Theory, 2017, 2017	155*	2017

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