Simon Shaolei Du

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Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPreverjeni e-poštni naslov na princeton.edu
Ruosong WangAssistant Professor, Peking UniversityPreverjeni e-poštni naslov na pku.edu.cn
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganPreverjeni e-poštni naslov na umich.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityPreverjeni e-poštni naslov na cmu.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoPreverjeni e-poštni naslov na ttic.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityPreverjeni e-poštni naslov na cs.cmu.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityPreverjeni e-poštni naslov na cs.princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUPreverjeni e-poštni naslov na cs.cmu.edu
Liwei WangProfessor, Peking UniversityPreverjeni e-poštni naslov na cis.pku.edu.cn
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAPreverjeni e-poštni naslov na ee.ucla.edu
Yuandong TianResearch Scientist, Meta AI (FAIR)Preverjeni e-poštni naslov na fb.com
Sham M KakadeHarvard UniversityPreverjeni e-poštni naslov na seas.harvard.edu
Xiyu ZhaiPreverjeni e-poštni naslov na mit.edu
Yining WangAssistant professor, Warrington College of Business, University of FloridaPreverjeni e-poštni naslov na warrington.ufl.edu
Zhang ZihanTsinghua UniversityPreverjeni e-poštni naslov na mails.tsinghua.edu.cn
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyPreverjeni e-poštni naslov na cs.berkeley.edu
Kevin JamiesonAssociate Professor, University of WashingtonPreverjeni e-poštni naslov na cs.washington.edu
Qiwen CuiPhD, University of WashingtonPreverjeni e-poštni naslov na uw.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityPreverjeni e-poštni naslov na stat.cmu.edu
Keyulu XuMITPreverjeni e-poštni naslov na mit.edu

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Simon Shaolei Du

Druga imenaSimon S. Du, Simon Du

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

Preverjeni e-poštni naslov na cs.washington.edu - Domača stran

Machine Learning


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
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	1340	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	1047	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	980	2019
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh International Conference on Learning Representations 2019, 2018	808	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	343	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	296	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	292	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	283	2019
On the power of over-parametrization in neural networks with quadratic activation SS Du, JD Lee International Conference on Machine Learning 2018, 2018	283	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	271	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	263	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	230	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	226	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	182	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	156*	2017

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