Simon Shaolei Du

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Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityEmail verificata su princeton.edu
Ruosong WangAssistant Professor, Peking UniversityEmail verificata su pku.edu.cn
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganEmail verificata su umich.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityEmail verificata su cmu.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoEmail verificata su ttic.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityEmail verificata su cs.cmu.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityEmail verificata su cs.princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUEmail verificata su cs.cmu.edu
Liwei WangProfessor, Peking UniversityEmail verificata su cis.pku.edu.cn
Xiyu ZhaiEmail verificata su mit.edu
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAEmail verificata su ee.ucla.edu
Yuandong TianResearch Scientist, Meta AI (FAIR)Email verificata su fb.com
Sham M KakadeHarvard UniversityEmail verificata su seas.harvard.edu
Zhang ZihanUniversity of WashingtonEmail verificata su uw.edu
Yining WangAssistant professor, Warrington College of Business, University of FloridaEmail verificata su warrington.ufl.edu
Kevin JamiesonAssociate Professor, University of WashingtonEmail verificata su cs.washington.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyEmail verificata su cs.berkeley.edu
Qiwen CuiPhD, University of WashingtonEmail verificata su uw.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityEmail verificata su stat.cmu.edu
Keyulu XuMITEmail verificata su mit.edu

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Simon Shaolei Du

Altri nomiSimon S. Du, Simon Du

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

Email verificata su cs.washington.edu - Home page

Machine Learning


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	190	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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