Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of Xiaojin Zhu'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 Xiaojin Zhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaojin Zhu more than expected).
This network shows the impact of papers produced by Xiaojin Zhu. 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 Xiaojin Zhu. The network helps show where Xiaojin Zhu may publish in the future.
Co-authorship network of co-authors of Xiaojin Zhu
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaojin Zhu.
A scholar is included among the top collaborators of Xiaojin Zhu 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 Xiaojin Zhu. Xiaojin Zhu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Xuezhou, et al.. (2020). The Teaching Dimension of Q-learning. arXiv (Cornell University).3 indexed citations
4.
Ghosh, Shalini, Patrick Lincoln, Ashish Tiwari, & Xiaojin Zhu. (2017). Trusted Machine Learning: Model Repair and Data Repair for Probabilistic Models.. National Conference on Artificial Intelligence.4 indexed citations
5.
Jun, Kwang-Sung, Kevin Jamieson, Robert D. Nowak, & Xiaojin Zhu. (2016). Top Arm Identification in Multi-Armed Bandits with Batch Arm Pulls. International Conference on Artificial Intelligence and Statistics. 139–148.6 indexed citations
6.
Mei, Shike & Xiaojin Zhu. (2015). The Security of Latent Dirichlet Allocation. International Conference on Artificial Intelligence and Statistics. 681–689.26 indexed citations
7.
Dasarathy, Gautam, Robert D. Nowak, & Xiaojin Zhu. (2015). S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification. Journal of Machine Learning Research. 40(2015). 503–522.7 indexed citations
8.
Patil, Kaustubh R., et al.. (2014). Optimal Teaching for Limited-Capacity Human Learners. UCL Discovery (University College London). 27. 2465–2473.34 indexed citations
9.
Zhu, Xiaojin. (2013). Persistent homology: an introduction and a new text representation for natural language processing. International Joint Conference on Artificial Intelligence. 1953–1959.37 indexed citations
10.
Xu, Junming, Benjamin Burchfiel, Xiaojin Zhu, & Amy Bellmore. (2013). An Examination of Regret in Bullying Tweets. North American Chapter of the Association for Computational Linguistics. 697–702.22 indexed citations
11.
Settles, Burr & Xiaojin Zhu. (2012). Behavioral Factors in Interactive Training of Text Classifiers. North American Chapter of the Association for Computational Linguistics. 563–567.7 indexed citations
12.
Wahba, Grace, et al.. (2011). Learning Higher-Order Graph Structure with Features by Structure Penalty. Neural Information Processing Systems. 24. 253–261.9 indexed citations
13.
Goldberg, Andrew B., Ben Recht, Junming Xu, Robert D. Nowak, & Xiaojin Zhu. (2010). Transduction with Matrix Completion: Three Birds with One Stone. Neural Information Processing Systems. 23. 757–765.132 indexed citations
14.
Rogers, Timothy J., Charles W. Kalish, Joseph G. Harrison, Xiaojin Zhu, & Bryan R. Gibson. (2010). Humans Learn Using Manifolds, Reluctantly. Neural Information Processing Systems. 23. 730–738.7 indexed citations
Singh, Aarti, Robert D. Nowak, & Xiaojin Zhu. (2008). Unlabeled data: Now it helps, now it doesn't. Neural Information Processing Systems. 21. 1513–1520.116 indexed citations
17.
Goldberg, Andrew B., Xiaojin Zhu, & Stephen J. Wright. (2007). Dissimilarity in Graph-Based Semi-Supervised Classification. International Conference on Artificial Intelligence and Statistics. 155–162.67 indexed citations
18.
Zhu, Xiaojin, et al.. (2007). A text-to-picture synthesis system for augmenting communication. National Conference on Artificial Intelligence. 1590–1595.60 indexed citations
19.
Zhu, Xiaojin, et al.. (2007). Humans perform semi-supervised classification too. National Conference on Artificial Intelligence. 864–869.31 indexed citations
20.
Zhu, Xiaojin, John Lafferty, & Zoubin Ghahramani. (2003). Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. UCL Discovery (University College London).317 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.