Stebėti
Simon Shaolei Du
Simon Shaolei Du
Kiti vardaiSimon S. Du, Simon Du
Assistant Professor, School of Computer Science and Engineering, University of Washington
Patvirtintas el. paštas cs.washington.edu - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
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
12632018
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
9892019
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
9212019
Gradient descent provably optimizes over-parameterized neural networks
SS Du, X Zhai, B Poczos, A Singh
International Conference on Learning Representations 2019, 2018
7752018
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
3132021
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
2832017
On the power of over-parametrization in neural networks with quadratic activation
SS Du, JD Lee
International Conference on Machine Learning 2018, 2018
2752018
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
2682019
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
2642019
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
2562019
Few-shot learning via learning the representation, provably
SS Du, W Hu, SM Kakade, JD Lee, Q Lei
International Conference on Learning Representations, 2021
2532021
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
Understanding the acceleration phenomenon via high-resolution differential equations
B Shi, SS Du, MI Jordan, WJ Su
Mathematical Programming, 1-70, 2022
2422022
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
2302019
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
2162021
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
2092018
Stochastic variance reduction methods for policy evaluation
SS Du, J Chen, L Li, L Xiao, D Zhou
International Conference on Machine Learning 2017, 2017
1922017
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
1762019
Optimism in reinforcement learning with generalized linear function approximation
Y Wang, R Wang, SS Du, A Krishnamurthy
International Conference on Learning Representations, 2021
1632021
Computationally efficient robust estimation of sparse functionals
SS Du, S Balakrishnan, A Singh
Conference on Learning Theory, 2017, 2017
149*2017
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Straipsniai 1–20