Simon Shaolei Du
Simon Shaolei 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 provably optimizes over-parameterized neural networks
SS Du, X Zhai, B Poczos, A Singh
International Conference on Learning Representations 2019, 2018
5592018
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
5262018
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
4062019
On exact computation with an infinitely wide neural net
S Arora, SS Du, W Hu, Z Li, R Salakhutdinov, R Wang
arXiv preprint arXiv:1904.11955, 2019
3342019
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
1652017
On the power of over-parametrization in neural networks with quadratic activation
SS Du, JD Lee
International Conference on Machine Learning 2018, 2018
1582018
Gradient descent can take exponential time to escape saddle points
SS Du, C Jin, JD Lee, MI Jordan, B Poczos, A Singh
arXiv preprint arXiv:1705.10412, 2017
1582017
When is a convolutional filter easy to learn?
SS Du, JD Lee, Y Tian
International Conference on Learning Representations 2018, 2017
1052017
Stochastic variance reduction methods for policy evaluation
SS Du, J Chen, L Li, L Xiao, D Zhou
International Conference on Machine Learning 2017, 2017
1042017
Computationally efficient robust estimation of sparse functionals
SS Du, S Balakrishnan, A Singh
Conference on Learning Theory, 2017, 2017
103*2017
Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced
SS Du, W Hu, JD Lee
arXiv preprint arXiv:1806.00900, 2018
832018
Understanding the acceleration phenomenon via high-resolution differential equations
B Shi, SS Du, MI Jordan, WJ Su
Mathematical Programming, 1-70, 2021
782021
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
742019
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
732019
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
702019
Linear convergence of the primal-dual gradient method for convex-concave saddle point problems without strong convexity
SS Du, W Hu
International Conference on Artificial Intelligence and Statistics 2019, 2018
702018
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
692019
Stochastic zeroth-order optimization in high dimensions
Y Wang, S Du, S Balakrishnan, A Singh
International Conference on Artificial Intelligence and Statistics 2018, 2017
642017
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
632019
Acceleration via symplectic discretization of high-resolution differential equations
B Shi, SS Du, WJ Su, MI Jordan
arXiv preprint arXiv:1902.03694, 2019
522019
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