Kai-Yang Chiang

720 total citations
9 papers, 465 citations indexed

About

Kai-Yang Chiang is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computational Mechanics. According to data from OpenAlex, Kai-Yang Chiang has authored 9 papers receiving a total of 465 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Statistical and Nonlinear Physics and 4 papers in Computational Mechanics. Recurrent topics in Kai-Yang Chiang's work include Sparse and Compressive Sensing Techniques (4 papers), Complex Network Analysis Techniques (4 papers) and Advanced Graph Neural Networks (4 papers). Kai-Yang Chiang is often cited by papers focused on Sparse and Compressive Sensing Techniques (4 papers), Complex Network Analysis Techniques (4 papers) and Advanced Graph Neural Networks (4 papers). Kai-Yang Chiang collaborates with scholars based in United States. Kai-Yang Chiang's co-authors include Inderjit S. Dhillon, Cho‐Jui Hsieh, Nagarajan Natarajan, Ambuj Tewari, Joyce Jiyoung Whang, Si Si and Nikhil Rao and has published in prestigious journals such as Journal of Machine Learning Research, Neural Information Processing Systems and International Conference on Machine Learning.

In The Last Decade

Kai-Yang Chiang

9 papers receiving 443 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kai-Yang Chiang United States	8	298	267	97	84	70	9	465
Christopher Musco United States	11	222 0.7×	59 0.2×	41 0.4×	81 1.0×	84 1.2×	26	415
Chaobo He China	11	296 1.0×	234 0.9×	87 0.9×	17 0.2×	161 2.3×	57	514
Weihong Qian China	10	295 1.0×	117 0.4×	100 1.0×	19 0.2×	312 4.5×	16	561
Károly Csalogány Hungary	11	325 1.1×	165 0.6×	359 3.7×	20 0.2×	139 2.0×	15	615
Prem Gopalan United States	8	270 0.9×	197 0.7×	189 1.9×	15 0.2×	75 1.1×	12	643
Weixiang Shao United States	10	242 0.8×	59 0.2×	134 1.4×	13 0.2×	154 2.2×	15	383
Aiyou Chen United States	9	225 0.8×	348 1.3×	24 0.2×	22 0.3×	45 0.6×	19	623
Anup Rao United States	10	272 0.9×	140 0.5×	69 0.7×	12 0.1×	84 1.2×	28	413
Andreas Lommatzsch Germany	7	317 1.1×	288 1.1×	112 1.2×	8 0.1×	57 0.8×	26	530

Countries citing papers authored by Kai-Yang Chiang

Since Specialization

Citations

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

Fields of papers citing papers by Kai-Yang Chiang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai-Yang Chiang. 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 Kai-Yang Chiang. The network helps show where Kai-Yang Chiang may publish in the future.

Co-authorship network of co-authors of Kai-Yang Chiang

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Chiang, Kai-Yang, Inderjit S. Dhillon, & Cho‐Jui Hsieh. (2018). Using Side Information to Reliably Learn Low-Rank Matrices from Missing and Corrupted Observations. Journal of Machine Learning Research. 19(76). 1–35. 22 indexed citations

2.

Chiang, Kai-Yang, Cho‐Jui Hsieh, & Inderjit S. Dhillon. (2017). Rank Aggregation and Prediction with Item Features. International Conference on Artificial Intelligence and Statistics. 748–756. 1 indexed citations

3.

Chiang, Kai-Yang, Cho‐Jui Hsieh, & Inderjit S. Dhillon. (2016). Robust principal component analysis with side information. International Conference on Machine Learning. 2291–2299. 23 indexed citations

4.

Si, Si, Kai-Yang Chiang, Cho‐Jui Hsieh, Nikhil Rao, & Inderjit S. Dhillon. (2016). Goal-Directed Inductive Matrix Completion. 1165–1174. 25 indexed citations

5.

Chiang, Kai-Yang, Cho‐Jui Hsieh, & Inderjit S. Dhillon. (2015). Matrix completion with noisy side information. Neural Information Processing Systems. 28. 3447–3455. 56 indexed citations

6.

Chiang, Kai-Yang, Cho‐Jui Hsieh, Nagarajan Natarajan, Ambuj Tewari, & Inderjit S. Dhillon. (2013). Prediction and Clustering in Signed Networks: A Local to Global Perspective. Journal of Machine Learning Research. 15(1). 1177–1213. 78 indexed citations

7.

Hsieh, Cho‐Jui, Kai-Yang Chiang, & Inderjit S. Dhillon. (2012). Low rank modeling of signed networks. 507–515. 101 indexed citations

8.

Chiang, Kai-Yang, Joyce Jiyoung Whang, & Inderjit S. Dhillon. (2012). Scalable clustering of signed networks using balance normalized cut. 615–624. 55 indexed citations

9.

Chiang, Kai-Yang, Nagarajan Natarajan, Ambuj Tewari, & Inderjit S. Dhillon. (2011). Exploiting longer cycles for link prediction in signed networks. 1157–1162. 104 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.

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