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.
Collaborative filtering and deep learning based recommendation system for cold start items
This map shows the geographic impact of Yi 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 Yi Zhou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yi Zhou more than expected).
This network shows the impact of papers produced by Yi 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 Yi Zhou. The network helps show where Yi Zhou may publish in the future.
Co-authorship network of co-authors of Yi Zhou
This figure shows the co-authorship network connecting the top 25 collaborators of Yi Zhou.
A scholar is included among the top collaborators of Yi 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 Yi Zhou. Yi Zhou is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wang, Zhe, Kaiyi Ji, Yi Zhou, Yingbin Liang, & Vahid Tarokh. (2019). SpiderBoost and Momentum: Faster Variance Reduction Algorithms. Neural Information Processing Systems. 32. 2403–2413.23 indexed citations
10.
Zhou, Yi, Junjie Yang, Huishuai Zhang, Yingbin Liang, & Vahid Tarokh. (2019). SGD Converges to Global Minimum in Deep Learning via Star-convex Path.. International Conference on Learning Representations.6 indexed citations
11.
Lan, Guanghui, Zhize Li, & Yi Zhou. (2019). A unified variance-reduced accelerated gradient method for convex optimization. Singapore Management University Institutional Knowledge (InK) (Singapore Management University). 32. 10462–10472.3 indexed citations
12.
Wang, Zhe, Yi Zhou, Yingbin Liang, & Guanghui Lan. (2018). Sample Complexity of Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization.. arXiv (Cornell University).2 indexed citations
13.
Zhou, Yi & Yingbin Liang. (2018). Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties. arXiv (Cornell University).14 indexed citations
14.
Xu, Tengyu, Yi Zhou, Kaiyi Ji, & Yingbin Liang. (2018). Convergence of SGD in Learning ReLU Models with Separable Data.. arXiv (Cornell University).2 indexed citations
Li, Qunwei, Yi Zhou, Yingbin Liang, & Pramod K. Varshney. (2017). Convergence analysis of proximal gradient with momentum for nonconvex optimization. International Conference on Machine Learning. 2111–2119.6 indexed citations
17.
Lan, Guanghui, et al.. (2017). Conditional Accelerated Lazy Stochastic Gradient Descent. International Conference on Machine Learning. 1965–1974.1 indexed citations
18.
Zhang, Huishuai, Yi Zhou, & Yingbin Liang. (2015). Analysis of robust PCA via local incoherence. Neural Information Processing Systems. 28. 1819–1827.7 indexed citations
19.
Zhou, Yi. (2009). Research on Dynamic Web Service Selection based Multi-QoS constraints. Microcomputer Information.2 indexed citations
20.
Zhou, Yi. (2002). Mathematics Education Markup Language. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2002(1). 2783–2786.1 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.