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Simon Shaolei Du
Simon Shaolei Du
Other namesSimon S. Du, Simon Du
Assistant Professor, School of Computer Science and Engineering, University of Washington
Verified email at cs.washington.edu - Homepage
Title
Cited by
Cited by
Year
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
13402018
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
10472019
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
9802019
Gradient descent provably optimizes over-parameterized neural networks
SS Du, X Zhai, B Poczos, A Singh
International Conference on Learning Representations 2019, 2018
8082018
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
3432021
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
2962017
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
2922019
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
2832019
On the power of over-parametrization in neural networks with quadratic activation
SS Du, JD Lee
International Conference on Machine Learning 2018, 2018
2832018
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
2712019
Few-shot learning via learning the representation, provably
SS Du, W Hu, SM Kakade, JD Lee, Q Lei
International Conference on Learning Representations, 2021
2672021
Understanding the acceleration phenomenon via high-resolution differential equations
B Shi, SS Du, MI Jordan, WJ Su
Mathematical Programming, 1-70, 2022
2632022
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
2472017
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
2412019
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
2302021
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
2262018
Stochastic variance reduction methods for policy evaluation
SS Du, J Chen, L Li, L Xiao, D Zhou
International Conference on Machine Learning 2017, 2017
2032017
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
1822019
Optimism in reinforcement learning with generalized linear function approximation
Y Wang, R Wang, SS Du, A Krishnamurthy
International Conference on Learning Representations, 2021
1722021
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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