Citations per year, relative to Lin F. Yang Lin F. Yang (= 1×)
peers
Bodhisattva Sen
Countries citing papers authored by Lin F. Yang
Since
Specialization
Citations
This map shows the geographic impact of Lin F. Yang'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 Lin F. Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lin F. Yang more than expected).
This network shows the impact of papers produced by Lin F. Yang. 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 Lin F. Yang. The network helps show where Lin F. Yang may publish in the future.
Co-authorship network of co-authors of Lin F. Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Lin F. Yang.
A scholar is included among the top collaborators of Lin F. Yang 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 Lin F. Yang. Lin F. Yang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yang, Lin F., et al.. (2021). Q-learning with Logarithmic Regret. International Conference on Artificial Intelligence and Statistics. 1576–1584.1 indexed citations
5.
Liang, Yingyu, et al.. (2020). Sketching Transformed Matrices with Applications to Natural Language Processing.. International Conference on Artificial Intelligence and Statistics. 467–481.1 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.
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
8.
Sidford, Aaron, Mengdi Wang, Lin F. Yang, & Yinyu Ye. (2019). Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. International Conference on Artificial Intelligence and Statistics. 2992–3002.2 indexed citations
9.
Chen, Zhehui, Xingguo Li, Lin F. Yang, Jarvis Haupt, & Tuo Zhao. (2019). On Constrained Nonconvex Stochastic Optimization: A Case Study for Generalized Eigenvalue Decomposition. International Conference on Artificial Intelligence and Statistics. 916–925.3 indexed citations
10.
Sidford, Aaron, Mengdi Wang, Xian Wu, Lin F. Yang, & Yinyu Ye. (2018). Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. neural information processing systems. 31. 5186–5196.26 indexed citations
11.
Yang, Lin F., Raman Arora, Vladimir Braverman, & Tuo Zhao. (2018). The Physical Systems Behind Optimization Algorithms. arXiv (Cornell University). 31. 4372–4381.2 indexed citations
12.
Yang, Lin F., et al.. (2018). Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. arXiv (Cornell University). 31. 3496–3506.2 indexed citations
13.
Chen, Zhehui, Lin F. Yang, Chris Junchi Li, & Tuo Zhao. (2017). Online partial least square optimization: dropping convexity for better efficiency and scalability. International Conference on Machine Learning. 777–786.2 indexed citations
14.
Yang, Lin F., Vladimir Braverman, Tuo Zhao, & Mengdi Wang. (2017). Dynamic Factorization and Partition of Complex Networks.. arXiv (Cornell University).2 indexed citations
15.
Hanneke, Steve & Lin F. Yang. (2015). Minimax analysis of active learning. arXiv (Cornell University). 16(1). 3487–3602.15 indexed citations
16.
Hanneke, Steve & Lin F. Yang. (2015). Statistical Learning under Nonstationary Mixing Processes. International Conference on Artificial Intelligence and Statistics. 1678–1686.1 indexed citations
17.
Yang, Lin F. & Steve Hanneke. (2013). Activized Learning with Uniform Classification Noise. International Conference on Machine Learning. 370–378.2 indexed citations
18.
Yang, Lin F.. (2011). Active Learning with a Drifting Distribution. Neural Information Processing Systems. 24. 2079–2087.7 indexed citations
19.
Hanneke, Steve & Lin F. Yang. (2010). Negative Results for Active Learning with Convex Losses. International Conference on Artificial Intelligence and Statistics. 321–325.5 indexed citations
20.
Yang, Lin F., et al.. (2003). A L2E-Based QoS Forecasting Algorithm for Dynamic, Distributed Real-Time Systems.. Parallel and Distributed Processing Techniques and Applications. 424–429.1 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.