Simon Shaolei Du
Simon Shaolei Du
Postdoc Fellow at Institute for Advanced Study
Verified email at cs.cmu.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
2162018
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
1952018
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
1192017
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
S Arora, SS Du, W Hu, Z Li, R Wang
ICML 2019, 2019
1152019
On the power of over-parametrization in neural networks with quadratic activation
SS Du, JD Lee
International Conference on Machine Learning 2018, 2018
912018
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, 1067-1077, 2017
852017
When is a convolutional filter easy to learn?
SS Du, JD Lee, Y Tian
International Conference on Learning Representations 2018, 2017
762017
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, 8139-8148, 2019
662019
Stochastic variance reduction methods for policy evaluation
SS Du, J Chen, L Li, L Xiao, D Zhou
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
632017
Computationally efficient robust estimation of sparse functionals
SS Du, S Balakrishnan, A Singh
Conference on Learning Theory, 2017, 2017
59*2017
Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced
SS Du, W Hu, JD Lee
Advances in Neural Information Processing Systems, 384-395, 2018
352018
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
332018
Understanding the acceleration phenomenon via high-resolution differential equations
B Shi, SS Du, MI Jordan, WJ Su
arXiv preprint arXiv:1810.08907, 2018
322018
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
SS Du, K Hou, B Póczos, R Salakhutdinov, R Wang, K Xu
arXiv preprint arXiv:1905.13192, 2019
31*2019
Stochastic zeroth-order optimization in high dimensions
Y Wang, S Du, S Balakrishnan, A Singh
International Conference on Artificial Intelligence and Statistics 2018, 2017
262017
An improved gap-dependency analysis of the noisy power method
MF Balcan, SS Du, Y Wang, AW Yu
Conference on Learning Theory, 284-309, 2016
262016
Hypothesis Transfer Learning via Transformation Functions
SS Du, J Koushik, A Singh, B Poczos
Advances in Neural Information Processing Systems, 2017, 2016
172016
High-throughput robotic phenotyping of energy sorghum crops
S Vijayarangan, P Sodhi, P Kini, J Bourne, S Du, H Sun, B Poczos, ...
Field and Service Robotics, 99-113, 2018
152018
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?
S Du, Y Wang, X Zhai, S Balakrishnan, R Salakhutdinov, A Singh
stat 1050, 30, 2019
13*2019
Width provably matters in optimization for deep linear neural networks
SS Du, W Hu
arXiv preprint arXiv:1901.08572, 2019
132019
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