This map shows the geographic impact of Xiangru Lian'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 Xiangru Lian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiangru Lian more than expected).
This network shows the impact of papers produced by Xiangru Lian. 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 Xiangru Lian. The network helps show where Xiangru Lian may publish in the future.
Co-authorship network of co-authors of Xiangru Lian
This figure shows the co-authorship network connecting the top 25 collaborators of Xiangru Lian.
A scholar is included among the top collaborators of Xiangru Lian 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 Xiangru Lian. Xiangru Lian is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
13 of 13 papers shown
1.
Jiang, Jiawei, Binhang Yuan, Ce Zhang, et al.. (2021). Bagua. Proceedings of the VLDB Endowment. 15(4). 804–813.18 indexed citations
Lian, Xiangru & Ji Liu. (2019). Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization. International Conference on Artificial Intelligence and Statistics. 3254–3263.14 indexed citations
4.
Hu, Wenqing, et al.. (2019). Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent. Neural Information Processing Systems. 32. 6926–6935.5 indexed citations
5.
Tang, Hanlin, Xiangru Lian, Ming Yan, Ce Zhang, & Ji Liu. (2018). $D^2$: Decentralized Training over Decentralized Data. International Conference on Machine Learning. 4848–4856.1 indexed citations
6.
Xiong, Jiechao, Qing Wang, Zhuoran Yang, et al.. (2018). PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE.1 indexed citations
7.
Lian, Xiangru, Ce Zhang, Huan Zhang, et al.. (2017). Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent. Neural Information Processing Systems. 30. 5330–5340.188 indexed citations
8.
Lian, Xiangru, Mengdi Wang, & Ji Liu. (2017). Finite-sum composition optimization via variance reduced gradient descent. 1159–1167.17 indexed citations
9.
You, Yang, Xiangru Lian, Ji Liu, et al.. (2016). Asynchronous Parallel Greedy Coordinate Descent. Neural Information Processing Systems. 29. 4682–4690.15 indexed citations
10.
Zhang, Wei, Suyog Gupta, Xiangru Lian, & Ji Liu. (2016). Staleness-aware async-SGD for distributed deep learning. International Joint Conference on Artificial Intelligence. 2350–2356.100 indexed citations
11.
Lian, Xiangru, Huan Zhang, Cho‐Jui Hsieh, Yijun Huang, & Ji Liu. (2016). A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order. Neural Information Processing Systems. 29. 3054–3062.19 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.