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 Zhi‐Hua Zhou'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 Zhi‐Hua Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Zhi‐Hua Zhou more than expected).
This network shows the impact of papers produced by Zhi‐Hua Zhou. 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 Zhi‐Hua Zhou. The network helps show where Zhi‐Hua Zhou may publish in the future.
Co-authorship network of co-authors of Zhi‐Hua Zhou
This figure shows the co-authorship network connecting the top 25 collaborators of Zhi‐Hua Zhou.
A scholar is included among the top collaborators of Zhi‐Hua Zhou 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 Zhi‐Hua Zhou. Zhi‐Hua Zhou is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhou, Zhi‐Hua, et al.. (2021). Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable. Neural Information Processing Systems. 34.1 indexed citations
Huang, Sheng-Jun, et al.. (2020). Cost-effectively Identifying Causal Effects When Only Response Variable is Observable.. International Conference on Machine Learning. 10060–10069.1 indexed citations
6.
Zhou, Zhi‐Hua, et al.. (2019). Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin. Bristol Research (University of Bristol). 6840–6849.16 indexed citations
7.
Yang, Liang, et al.. (2019). A Refined Margin Distribution Analysis for Forest Representation Learning. Neural Information Processing Systems. 32. 5530–5540.5 indexed citations
8.
Yu, Yang, et al.. (2019). Bridging Machine Learning and Logical Reasoning by Abductive Learning. Neural Information Processing Systems. 32. 2811–2822.47 indexed citations
Zhang, Lijun, et al.. (2018). Adaptive Online Learning in Dynamic Environments. arXiv (Cornell University). 31. 1323–1333.5 indexed citations
12.
Yang, Tianbao, et al.. (2017). Improved Dynamic Regret for Non-degenerate Functions. arXiv (Cornell University). 30. 732–741.16 indexed citations
13.
Wang, Lu & Zhi‐Hua Zhou. (2016). Cost-saving effect of crowdsourcing learning. International Joint Conference on Artificial Intelligence. 2111–2117.16 indexed citations
14.
Li, Yufeng, et al.. (2016). Graph quality judgement: a large margin expedition. International Joint Conference on Artificial Intelligence. 1725–1731.28 indexed citations
15.
Zhu, Yue, Jianxin Wu, Yuan Jiang, & Zhi‐Hua Zhou. (2014). Learning with Augmented Multi-Instance View. Asian Conference on Machine Learning. 234–249.1 indexed citations
16.
Zhou, Zhi‐Hua, et al.. (2013). Co-Training with Insufficient Views. Asian Conference on Machine Learning. 467–482.20 indexed citations
17.
Xu, Miao, Rong Jin, & Zhi‐Hua Zhou. (2013). Speedup Matrix Completion with Side Information: Application to Multi-Label Learning. Neural Information Processing Systems. 26. 2301–2309.148 indexed citations
Li, Yu-Feng & Zhi‐Hua Zhou. (2011). Towards Making Unlabeled Data Never Hurt. International Conference on Machine Learning. 1081–1088.58 indexed citations
20.
Li, Ming, Xiaobing Xue, & Zhi‐Hua Zhou. (2009). Exploiting multi-modal interactions: a unified framework. International Joint Conference on Artificial Intelligence. 1120–1125.9 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.