This map shows the geographic impact of Jason D. Lee'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 Jason D. Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason D. Lee more than expected).
This network shows the impact of papers produced by Jason D. Lee. 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 Jason D. Lee. The network helps show where Jason D. Lee may publish in the future.
Co-authorship network of co-authors of Jason D. Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Jason D. Lee.
A scholar is included among the top collaborators of Jason D. Lee 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 Jason D. Lee. Jason D. Lee is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Lee, Jason D., et al.. (2020). Towards Understanding Hierarchical Learning: Benefits of Neural Representations. Neural Information Processing Systems. 33. 22134–22145.1 indexed citations
2.
Zhang, Weizhong, et al.. (2020). How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 33. 3797–3807.1 indexed citations
3.
Gao, Ruiqi, Tianle Cai, Haochuan Li, et al.. (2019). Convergence of Adversarial Training in Overparametrized Neural Networks. arXiv (Cornell University). 32. 13009–13020.4 indexed citations
4.
Nouiehed, Maher, et al.. (2019). Solving a class of non-convex min-max games using iterative first order methods. neural information processing systems. 32. 14905–14916.36 indexed citations
5.
Wei, Colin, Jason D. Lee, Qiang Liu, & Tengyu Ma. (2019). Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel. neural information processing systems. 32. 9709–9721.16 indexed citations
6.
Wei, Colin, Jason D. Lee, Qiang Liu, & Tengyu Ma. (2018). On the Margin Theory of Feedforward Neural Networks. arXiv (Cornell University).12 indexed citations
7.
Wu, Chenwei, et al.. (2018). No Spurious Local Minima in a Two Hidden Unit ReLU Network. International Conference on Learning Representations.1 indexed citations
8.
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
9.
Kakade, Sham M. & Jason D. Lee. (2018). Provably Correct Automatic Sub-Differentiation for Qualified Programs. neural information processing systems. 31. 7125–7135.1 indexed citations
10.
Gunasekar, Suriya, Jason D. Lee, Daniel Soudry, & Nathan Srebro. (2018). Characterizing Implicit Bias in Terms of Optimization Geometry. International Conference on Machine Learning. 2932–2955.29 indexed citations
11.
Lee, Jason D., Qiang Liu, Yuekai Sun, & Jonathan Taylor. (2017). Communication-efficient sparse regression. Journal of Machine Learning Research. 18(5). 115–144.69 indexed citations
12.
Liu, Qiang & Jason D. Lee. (2017). Black-box importance sampling. International Conference on Artificial Intelligence and Statistics. 952–961.9 indexed citations
13.
Lee, Jason D., Qihang Lin, Tengyu Ma, & Tianbao Yang. (2017). Distributed stochastic variance reduced gradient methods by sampling extra data with replacement. Journal of Machine Learning Research. 18(122). 1–43.31 indexed citations
14.
Lee, Jason D., Max Simchowitz, Michael I. Jordan, & Benjamin Recht. (2016). Gradient Descent Only Converges to Minimizers. Conference on Learning Theory. 49. 1246–1257.147 indexed citations
15.
Lee, Jason D., Yuekai Sun, & Jonathan Taylor. (2015). Evaluating the statistical significance of biclusters. Neural Information Processing Systems. 28. 1324–1332.6 indexed citations
16.
Lee, Jason D., Tengyu Ma, & Qihang Lin. (2015). Distributed Stochastic Variance Reduced Gradient Methods.. arXiv (Cornell University).5 indexed citations
17.
Lee, Jason D., Dennis L. Sun, Yuekai Sun, & Jonathan Taylor. (2013). Exact inference after model selection via the Lasso. arXiv (Cornell University).7 indexed citations
18.
Lee, Jason D. & Trevor Hastie. (2013). Structure Learning of Mixed Graphical Models. International Conference on Artificial Intelligence and Statistics. 31. 388–396.14 indexed citations
19.
Lee, Jason D., et al.. (2010). Creating metadata best practices for CONTENTdm users. Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign). 2010. 74–78.2 indexed citations
20.
Lee, Jason D., et al.. (2004). From Chaos to Cooperation: Teaching Analytic Evaluation with LINK-UP. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2004(1). 2755–2762.5 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.