This map shows the geographic impact of Simon S. Du's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Simon S. Du with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simon S. Du more than expected).
This network shows the impact of papers produced by Simon S. Du. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Simon S. Du. The network helps show where Simon S. Du may publish in the future.
Co-authorship network of co-authors of Simon S. Du
This figure shows the co-authorship network connecting the top 25 collaborators of Simon S. Du.
A scholar is included among the top collaborators of Simon S. Du based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Simon S. Du. Simon S. Du is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Xu, Keyulu, et al.. (2021). How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. International Conference on Learning Representations.6 indexed citations
3.
Yang, Lin F., et al.. (2021). Q-learning with Logarithmic Regret. International Conference on Artificial Intelligence and Statistics. 1576–1584.1 indexed citations
4.
Wang, Yining, Ruosong Wang, Simon S. Du, & Akshay Krishnamurthy. (2021). Optimism in Reinforcement Learning with Generalized Linear Function Approximation. arXiv (Cornell University).2 indexed citations
5.
Du, Simon S., et al.. (2021). Near Optimal Reward-Free Reinforcement Learning. International Conference on Machine Learning. 12402–12412.2 indexed citations
6.
Wang, Ruosong, Simon S. Du, Lin F. Yang, & Sham M. Kakade. (2020). Is Long Horizon RL More Difficult Than Short Horizon RL. Neural Information Processing Systems. 33. 9075–9085.1 indexed citations
7.
Xu, Keyulu, et al.. (2020). What Can Neural Networks Reason About. International Conference on Learning Representations.11 indexed citations
8.
Feng, Fei, Ruosong Wang, Wotao Yin, Simon S. Du, & Lin F. Yang. (2020). Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. Neural Information Processing Systems. 33. 22492–22504.1 indexed citations
9.
Wang, Ruosong, Peilin Zhong, Simon S. Du, Russ R. Salakhutdinov, & Lin F. Yang. (2020). Planning with General Objective Functions: Going Beyond Total Rewards. Neural Information Processing Systems. 33. 14486–14497.2 indexed citations
10.
Zhang, Yi, Orestis Plevrakis, Simon S. Du, et al.. (2020). Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. Neural Information Processing Systems. 33. 679–688.1 indexed citations
11.
Hu, Wei, et al.. (2020). Provable Benefits of Representation Learning in Linear Bandits.. arXiv (Cornell University).
12.
Arora, Sanjeev, Simon S. Du, Wei Hu, et al.. (2019). On Exact Computation with an Infinitely Wide Neural Net. arXiv (Cornell University). 32. 8139–8148.107 indexed citations
13.
Du, Simon S., Kangcheng Hou, Russ R. Salakhutdinov, et al.. (2019). Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. arXiv (Cornell University). 32. 5723–5733.16 indexed citations
14.
Du, Simon S., et al.. (2019). Acceleration via Symplectic Discretization of High-Resolution Differential Equations. Neural Information Processing Systems. 32. 5744–5752.2 indexed citations
15.
Du, Simon S. & Jason D. Lee. (2018). On the Power of Over-parametrization in Neural Networks with Quadratic Activation. International Conference on Machine Learning. 1329–1338.42 indexed citations
16.
Balakrishnan, Sivaraman, Simon S. Du, Jerry Li, & Aarti Singh. (2017). Computationally Efficient Robust Sparse Estimation in High Dimensions. Conference on Learning Theory. 169–212.10 indexed citations
17.
Du, Simon S., Jayanth Koushik, Aarti Singh, & Barnabás Póczos. (2017). Hypothesis Transfer Learning via Transformation Functions. Neural Information Processing Systems. 30. 574–584.9 indexed citations
18.
Du, Simon S., Yining Wang, & Aarti Singh. (2017). On the Power of Truncated SVD for General High-rank Matrix Estimation Problems. Neural Information Processing Systems. 30. 445–455.2 indexed citations
19.
Wang, Yining, Simon S. Du, Sivaraman Balakrishnan, & Aarti Singh. (2017). Stochastic Zeroth-order Optimization in High Dimensions.. International Conference on Artificial Intelligence and Statistics. 1356–1365.11 indexed citations
20.
Anderson, David G., et al.. (2015). Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nystrom Method. International Conference on Artificial Intelligence and Statistics. 19–27.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.