Ching-pei Lee

456 total citations
13 papers, 266 citations indexed

About

Ching-pei Lee is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Ching-pei Lee has authored 13 papers receiving a total of 266 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Computational Mechanics. Recurrent topics in Ching-pei Lee's work include Face and Expression Recognition (5 papers), Sparse and Compressive Sensing Techniques (4 papers) and Stochastic Gradient Optimization Techniques (3 papers). Ching-pei Lee is often cited by papers focused on Face and Expression Recognition (5 papers), Sparse and Compressive Sensing Techniques (4 papers) and Stochastic Gradient Optimization Techniques (3 papers). Ching-pei Lee collaborates with scholars based in Taiwan, United States and Singapore. Ching-pei Lee's co-authors include Chih‐Jen Lin, Dan Roth, Chen-Chung Fu, Julie C. Mitchell, Nathan Wlodarchak, Cheng‐I Chu, Stephen J. Wright, F. Michael Hoffmann, Huikun Zhang and Gene E. Ananiev and has published in prestigious journals such as PLoS Computational Biology, Neural Computation and Journal of Machine Learning Research.

In The Last Decade

Ching-pei Lee

12 papers receiving 256 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ching-pei Lee Taiwan 7 144 72 70 37 33 13 266
Jennifer Gillenwater United States 10 492 3.4× 115 1.6× 96 1.4× 41 1.1× 28 0.8× 19 622
Srinivas Vadrevu United States 9 202 1.4× 55 0.8× 137 2.0× 27 0.7× 46 1.4× 17 287
Sutanay Choudhury United States 11 131 0.9× 72 1.0× 66 0.9× 36 1.0× 86 2.6× 40 294
Mukund Narasimhan United States 10 159 1.1× 84 1.2× 49 0.7× 39 1.1× 36 1.1× 14 272
Lorie M. Liebrock United States 10 107 0.7× 50 0.7× 123 1.8× 122 3.3× 128 3.9× 42 309
Bo Long United States 11 258 1.8× 126 1.8× 96 1.4× 50 1.4× 20 0.6× 23 406
Kush Bhatia United States 6 279 1.9× 78 1.1× 44 0.6× 13 0.4× 15 0.5× 12 341
Predrag Janičić Serbia 10 165 1.1× 23 0.3× 34 0.5× 22 0.6× 60 1.8× 30 309
Yuli Zhao China 7 87 0.6× 229 3.2× 48 0.7× 14 0.4× 51 1.5× 33 364

Countries citing papers authored by Ching-pei Lee

Since Specialization
Citations

This map shows the geographic impact of Ching-pei Lee'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 Ching-pei Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ching-pei Lee more than expected).

Fields of papers citing papers by Ching-pei Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ching-pei Lee. 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 Ching-pei Lee. The network helps show where Ching-pei Lee may publish in the future.

Co-authorship network of co-authors of Ching-pei Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Ching-pei Lee. A scholar is included among the top collaborators of Ching-pei Lee 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 Ching-pei Lee. Ching-pei Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
1.
Lee, Ching-pei. (2023). Accelerating inexact successive quadratic approximation for regularized optimization through manifold identification. Mathematical Programming. 201(1-2). 599–633. 3 indexed citations
2.
Lee, Ching-pei, Po-Wei Wang, & Chih‐Jen Lin. (2022). Limited-memory common-directions method for large-scale optimization: convergence, parallelization, and distributed optimization. Mathematical Programming Computation. 14(3). 543–591.
3.
Li, Yusheng, Wei-Lin Chiang, & Ching-pei Lee. (2020). Manifold Identification for Ultimately Communication-Efficient Distributed Optimization. International Conference on Machine Learning. 1. 5842–5852. 1 indexed citations
4.
Zhang, Huikun, Spencer S. Ericksen, Ching-pei Lee, et al.. (2019). Predicting kinase inhibitors using bioactivity matrix derived informer sets. PLoS Computational Biology. 15(8). e1006813–e1006813. 7 indexed citations
5.
Lee, Ching-pei & Stephen J. Wright. (2019). First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems. International Conference on Machine Learning. 97. 3754–3762. 1 indexed citations
6.
Wang, Po-Wei, Ching-pei Lee, & Chih‐Jen Lin. (2019). The Common-directions Method for Regularized Empirical Risk Minimization. Journal of Machine Learning Research. 20(58). 1–49. 2 indexed citations
7.
Lee, Ching-pei & Dan Roth. (2015). Distributed Box-Constrained Quadratic Optimization for Dual Linear SVM. International Conference on Machine Learning. 987–996. 23 indexed citations
8.
Lee, Ching-pei & Chih‐Jen Lin. (2014). Large-Scale Linear RankSVM. Neural Computation. 26(4). 781–817. 78 indexed citations
9.
Lee, Ching-pei, et al.. (2014). Large-scale Kernel RankSVM. 812–820. 28 indexed citations
10.
Lee, Ching-pei, et al.. (2014). Large-scale logistic regression and linear support vector machines using spark. 519–528. 47 indexed citations
11.
Lee, Ching-pei & Chih‐Jen Lin. (2013). A Study on L2-Loss (Squared Hinge-Loss) Multiclass SVM. Neural Computation. 25(5). 1302–1323. 53 indexed citations
12.
Ferng, Chun-Sung, Chia-Hua Ho, Jyun‐Yu Jiang, et al.. (2012). A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012. 18 indexed citations
13.

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.

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2026