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.
AUC Maximization in the Era of Big Data and AI: A Survey
This map shows the geographic impact of Yiming Ying'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 Yiming Ying with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yiming Ying more than expected).
This network shows the impact of papers produced by Yiming Ying. 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 Yiming Ying. The network helps show where Yiming Ying may publish in the future.
Co-authorship network of co-authors of Yiming Ying
This figure shows the co-authorship network connecting the top 25 collaborators of Yiming Ying.
A scholar is included among the top collaborators of Yiming Ying 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 Yiming Ying. Yiming Ying is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
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.
Lei, Yunwen, et al.. (2021). Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss. University of Birmingham Research Portal (University of Birmingham). 2026–2034.3 indexed citations
7.
Yuan, Zhuoning, et al.. (2021). Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. International Conference on Machine Learning. 12219–12229.5 indexed citations
8.
Lyu, Siwei, Yanbo Fan, Yiming Ying, & Bao-Gang Hu. (2020). Average Top-k Aggregate Loss for Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(1). 76–86.5 indexed citations
Lei, Yunwen & Yiming Ying. (2020). Fine-Grained analysis of stability and generalization for SGD. International Conference on Machine Learning.1 indexed citations
Lyu, Siwei, et al.. (2016). Fast Convergence of Online Pairwise Learning Algorithms. International Conference on Artificial Intelligence and Statistics. 204–212.8 indexed citations
Li, Peng, Yiming Ying, & Colin Campbell. (2009). A variational approach to semi-supervised clustering. UCL Discovery (University College London).3 indexed citations
15.
Ying, Yiming & Colin Campbell. (2009). Generalization Bounds for Learning the Kernel Problem.. Conference on Learning Theory.11 indexed citations
16.
Ying, Yiming & Colin Campbell. (2008). Learning Coordinate Gradients with Multi-Task Kernels.. Conference on Learning Theory. 217–228.5 indexed citations
17.
Ying, Yiming & Ding‐Xuan Zhou. (2007). Learnability of Gaussians with Flexible Variances. Journal of Machine Learning Research. 8(9). 249–276.45 indexed citations
18.
Argyriou, Andreas A., Massimiliano Pontil, Yiming Ying, & Charles A. Micchelli. (2007). A Spectral Regularization Framework for Multi-Task Structure Learning. UCL Discovery (University College London). 20. 25–32.148 indexed citations
19.
Pontil, Massimiliano, et al.. (2005). Error analysis for online gradient descent algorithms in reproducing kernel Hilbert spaces. UCL Discovery (University College London).3 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.