Simon Shaolei Du
Simon Shaolei Du
Assistant Professor, School of Computer Science and Engineering, University of Washington
Geverifieerd e-mailadres voor cs.washington.edu - Homepage
Titel
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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
5872018
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
5512018
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
4312019
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
3592019
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
1742017
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
1662017
On the power of over-parametrization in neural networks with quadratic activation
SS Du, JD Lee
International Conference on Machine Learning 2018, 2018
1652018
Stochastic variance reduction methods for policy evaluation
SS Du, J Chen, L Li, L Xiao, D Zhou
International Conference on Machine Learning 2017, 2017
1082017
When is a convolutional filter easy to learn?
SS Du, JD Lee, Y Tian
International Conference on Learning Representations 2018, 2017
1062017
Computationally efficient robust estimation of sparse functionals
SS Du, S Balakrishnan, A Singh
Conference on Learning Theory, 2017, 2017
105*2017
Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced
SS Du, W Hu, JD Lee
arXiv preprint arXiv:1806.00900, 2018
892018
Understanding the acceleration phenomenon via high-resolution differential equations
B Shi, SS Du, MI Jordan, WJ Su
Mathematical Programming, 1-70, 2021
822021
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
812019
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
792019
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
752019
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
732019
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
722018
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
672019
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
Few-shot learning via learning the representation, provably
SS Du, W Hu, SM Kakade, JD Lee, Q Lei
arXiv preprint arXiv:2002.09434, 2020
552020
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Artikelen 1–20