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.
Efficient agglomerative hierarchical clustering
2014301 citationsAthman Bouguettaya, Qi Yu et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Qi Yu'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 Qi Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Qi Yu more than expected).
This network shows the impact of papers produced by Qi Yu. 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 Qi Yu. The network helps show where Qi Yu may publish in the future.
Co-authorship network of co-authors of Qi Yu
This figure shows the co-authorship network connecting the top 25 collaborators of Qi Yu.
A scholar is included among the top collaborators of Qi Yu 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 Qi Yu. Qi Yu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yu, Qi, et al.. (2021). A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning. Neural Information Processing Systems. 34.4 indexed citations
5.
Ying, Yiming, et al.. (2021). Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning. International Conference on Artificial Intelligence and Statistics. 2188–2196.4 indexed citations
6.
S, Li, Qi Yu, Li J, et al.. (2020). Experimental Study of Hepatocellular Carcinoma Treatment by Shikonin Through Regulating PKM2. SHILAP Revista de lepidopterología.5 indexed citations
7.
Zhao, Xujiang, et al.. (2020). Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning. Neural Information Processing Systems. 33. 17247–17257.9 indexed citations
Yu, Qi, et al.. (2020). Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains. Neural Information Processing Systems. 33. 2062–2073.1 indexed citations
10.
Yu, Qi, et al.. (2019). Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning. Neural Information Processing Systems. 32. 2282–2291.1 indexed citations
11.
Yu, Qi, et al.. (2019). Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning. International Conference on Machine Learning. 5769–5778.1 indexed citations
Yu, Qi. (2015). Study on Expending and Deepening of the Service of University Library Based on the New Media Environment. Sci-Tech Information Development & Economy.
15.
Alm, Cecilia Ovesdotter, et al.. (2014). Towards multimodal modeling of physicians' diagnostic confidence and self-awareness using medical narratives. International Conference on Computational Linguistics. 1718–1727.3 indexed citations
16.
Bahadori, Mohammad Taha, Qi Yu, & Yan Liu. (2014). Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning. Neural Information Processing Systems. 27. 3491–3499.80 indexed citations
Bouguettaya, Athman, Denis Gračanin, Qi Yu, et al.. (2006). Ubiquitous web services for e-government social services. RMIT Research Repository (RMIT University Library). 1–3.2 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.