Simon Shaolei Du

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Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPatvirtintas el. paštas princeton.edu
Ruosong WangAssistant Professor, Peking UniversityPatvirtintas el. paštas pku.edu.cn
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganPatvirtintas el. paštas umich.edu
Aarti SinghProfessor of Machine Learning Department, Carnegie Mellon UniversityPatvirtintas el. paštas cmu.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoPatvirtintas el. paštas ttic.edu
Barnabas PoczosAssociate professor, Carnegie Mellon UniversityPatvirtintas el. paštas cs.cmu.edu
Sanjeev AroraProfessor of Computer Science, Princeton UniversityPatvirtintas el. paštas cs.princeton.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUPatvirtintas el. paštas cs.cmu.edu
Liwei WangProfessor, Peking UniversityPatvirtintas el. paštas cis.pku.edu.cn
Xiyu ZhaiPatvirtintas el. paštas mit.edu
Lin F. Yang (杨林)Assistant Professor, Department of Electrical and Computer Engineering @ UCLAPatvirtintas el. paštas ee.ucla.edu
Yuandong TianResearch Scientist, Meta AI (FAIR)Patvirtintas el. paštas fb.com
Sham M KakadeHarvard UniversityPatvirtintas el. paštas seas.harvard.edu
Zhang ZihanUniversity of WashingtonPatvirtintas el. paštas uw.edu
Yining WangAssistant professor, Warrington College of Business, University of FloridaPatvirtintas el. paštas warrington.ufl.edu
Kevin JamiesonAssociate Professor, University of WashingtonPatvirtintas el. paštas cs.washington.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyPatvirtintas el. paštas cs.berkeley.edu
Qiwen CuiPhD, University of WashingtonPatvirtintas el. paštas uw.edu
Sivaraman BalakrishnanCarnegie Mellon UniversityPatvirtintas el. paštas stat.cmu.edu
Keyulu XuMITPatvirtintas el. paštas mit.edu

Stebėti

Simon Shaolei Du

Kiti vardaiSimon S. Du, Simon Du

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

Patvirtintas el. paštas cs.washington.edu - Pagrindinis puslapis

Machine Learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
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	1392	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	1090	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	1027	2019
Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh International Conference on Learning Representations 2019, 2018	836	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	362	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	251	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	243	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	216	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	182	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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