Simon Shaolei Du
Simon Shaolei Du
Assistant Professor, School of Computer Science and Engineering, University of Washington
Adresse e-mail validée de cs.washington.edu - Page d'accueil
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Gradient descent provably optimizes over-parameterized neural networks
SS Du, X Zhai, B Poczos, A Singh
International Conference on Learning Representations 2019, 2018
3112018
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
2822018
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
1922019
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, 8141-8150, 2019
1552019
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
1352017
On the power of over-parametrization in neural networks with quadratic activation
SS Du, JD Lee
International Conference on Machine Learning 2018, 2018
1082018
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
1042017
When is a convolutional filter easy to learn?
SS Du, JD Lee, Y Tian
International Conference on Learning Representations 2018, 2017
862017
Stochastic variance reduction methods for policy evaluation
SS Du, J Chen, L Li, L Xiao, D Zhou
International Conference on Machine Learning 2017, 2017
762017
Computationally efficient robust estimation of sparse functionals
SS Du, S Balakrishnan, A Singh
Conference on Learning Theory, 2017, 2017
73*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
452018
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
412018
Understanding the acceleration phenomenon via high-resolution differential equations
B Shi, SS Du, MI Jordan, WJ Su
arXiv preprint arXiv:1810.08907, 2018
382018
Stochastic zeroth-order optimization in high dimensions
Y Wang, S Du, S Balakrishnan, A Singh
International Conference on Artificial Intelligence and Statistics 2018, 2017
362017
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
292019
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
282016
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
272019
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
262019
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?
SS Du, Y Wang, X Zhai, S Balakrishnan, R Salakhutdinov, A Singh
Advances in Neural Information Processing Systems 2018, 2018
23*2018
Hypothesis Transfer Learning via Transformation Functions
SS Du, J Koushik, A Singh, B Poczos
Advances in Neural Information Processing Systems, 2017, 2016
222016
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