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.
Symmetric Cross Entropy for Robust Learning With Noisy Labels
2019555 citationsYisen Wang, Xingjun Ma et al.profile →
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
2018283 citationsPin‐Yu Chen, Huan Zhang et al.Proceedings of the AAAI Conference on Artificial Intelligenceprofile →
This map shows the geographic impact of Jinfeng Yi'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 Jinfeng Yi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jinfeng Yi more than expected).
This network shows the impact of papers produced by Jinfeng Yi. 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 Jinfeng Yi. The network helps show where Jinfeng Yi may publish in the future.
Co-authorship network of co-authors of Jinfeng Yi
This figure shows the co-authorship network connecting the top 25 collaborators of Jinfeng Yi.
A scholar is included among the top collaborators of Jinfeng Yi 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 Jinfeng Yi. Jinfeng Yi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Mingrui, Zhe Li, Xiaoyu Wang, Jinfeng Yi, & Tianbao Yang. (2018). Adaptive Negative Curvature Descent with Applications in Non-convex Optimization. Neural Information Processing Systems. 31. 4853–4862.2 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.
Lei, Qi, Jinfeng Yi, Roman Vaculín, Lingfei Wu, & Inderjit S. Dhillon. (2017). Similarity Preserving Representation Learning for Time Series Analysis.. arXiv (Cornell University).8 indexed citations
14.
Zhang, Lijun, Jinfeng Yi, & Rong Jin. (2014). Efficient Algorithms for Robust One-bit Compressive Sensing. International Conference on Machine Learning. 820–828.50 indexed citations
15.
Yi, Jinfeng, Lijun Zhang, Jun Wang, Rong Jin, & Anil K. Jain. (2014). A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data. International Conference on Machine Learning. 658–666.5 indexed citations
16.
Yi, Jinfeng, Lijun Zhang, Rong Jin, Qi Qian, & Anil K. Jain. (2013). Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix Completion. International Conference on Machine Learning. 1400–1408.25 indexed citations
17.
Zhang, Lijun, Jinfeng Yi, Rong Jin, Ming Lin, & Xiaofei He. (2013). Online Kernel Learning with a Near Optimal Sparsity Bound. International Conference on Machine Learning. 621–629.16 indexed citations
18.
Yi, Jinfeng, Rong Jin, Shaili Jain, Tianbao Yang, & Anil K. Jain. (2012). Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning. Neural Information Processing Systems. 25. 1772–1780.46 indexed citations
19.
Mahdavi, Mehrdad, Tianbao Yang, Rong Jin, Shenghuo Zhu, & Jinfeng Yi. (2012). Stochastic Gradient Descent with Only One Projection. Neural Information Processing Systems. 25. 494–502.19 indexed citations
20.
Yi, Jinfeng, Rong Jin, Anil K. Jain, & Shaili Jain. (2012). Crowdclustering with sparse pairwise labels: A matrix completion approach. National Conference on Artificial Intelligence. 47–53.28 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.