Po-Wei Wang

566 total citations
12 papers, 36 citations indexed

About

Po-Wei Wang is a scholar working on Artificial Intelligence, Management Science and Operations Research and Information Systems. According to data from OpenAlex, Po-Wei Wang has authored 12 papers receiving a total of 36 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Management Science and Operations Research and 3 papers in Information Systems. Recurrent topics in Po-Wei Wang's work include Bayesian Modeling and Causal Inference (3 papers), Sparse and Compressive Sensing Techniques (2 papers) and Machine Learning and Algorithms (2 papers). Po-Wei Wang is often cited by papers focused on Bayesian Modeling and Causal Inference (3 papers), Sparse and Compressive Sensing Techniques (2 papers) and Machine Learning and Algorithms (2 papers). Po-Wei Wang collaborates with scholars based in United States, Taiwan and Germany. Po-Wei Wang's co-authors include J. Zico Kolter, Chih‐Jen Lin, Daria Stepanova, Csaba Domokos, Inderjit S. Dhillon, Ching-pei Lee, Jiajing Xu, X.-G. Xia, Andrew Zhai and Cheng‐Lun Hsin and has published in prestigious journals such as Journal of Machine Learning Research, MRS Communications and Mathematical Programming Computation.

In The Last Decade

Po-Wei Wang

10 papers receiving 32 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Po-Wei Wang United States	4	26	8	8	5	3	12	36
Massih-Réza Amini France	5	25 1.0×	9 1.1×	8 1.0×	3 0.6×	1 0.3×	6	40
Joyce Cahoon United States	4	15 0.6×	11 1.4×	5 0.6×	8 1.6×	4 1.3×	11	31
E. Golobardes Spain	4	20 0.8×	7 0.9×	4 0.5×	8 1.6×	4 1.3×	5	37
Ward Beullens Switzerland	4	28 1.1×	8 1.0×	9 1.1×	5 1.0×	1 0.3×	9	38
John M. Schanck United States	4	34 1.3×	8 1.0×	10 1.3×	8 1.6×		6	37
Huazuo Gao	3	18 0.7×	6 0.8×	7 0.9×	7 1.4×	2 0.7×	3	59
Julia Hesse Switzerland	3	15 0.6×	8 1.0×	5 0.6×	7 1.4×		5	20
Saikrishna Badrinarayanan United States	4	23 0.9×	13 1.6×	5 0.6×	4 0.8×	2 0.7×	6	32
Zhangyue Yin China	4	44 1.7×	5 0.6×	11 1.4×	2 0.4×	4 1.3×	13	60
Yuncong Hu China	4	22 0.8×	12 1.5×	14 1.8×	6 1.2×		6	33

Countries citing papers authored by Po-Wei Wang

Since Specialization

Citations

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

Fields of papers citing papers by Po-Wei Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Po-Wei Wang. 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 Po-Wei Wang. The network helps show where Po-Wei Wang may publish in the future.

Co-authorship network of co-authors of Po-Wei Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Po-Wei Wang. A scholar is included among the top collaborators of Po-Wei Wang 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 Po-Wei Wang. Po-Wei Wang 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:

12 of 12 papers shown

1.

Wang, Po-Wei, et al.. (2024). Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training. ArXiv.org. 838–840. 1 indexed citations

2.

Xia, X.-G., et al.. (2023). TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest. 5249–5259. 4 indexed citations

3.

Hsin, Cheng‐Lun, et al.. (2023). MgAgSb thermoelectric composite and the effect of doping species. MRS Communications. 13(6). 1226–1231. 1 indexed citations

4.

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.

5.

Wang, Po-Wei, Daria Stepanova, Csaba Domokos, & J. Zico Kolter. (2020). Differentiable learning of numerical rules in knowledge graphs. International Conference on Learning Representations. 11 indexed citations

6.

Wang, Po-Wei & J. Zico Kolter. (2020). Community detection using fast low-cardinality semidefinite programming. arXiv (Cornell University). 33. 3374–3385. 1 indexed citations

7.

Wang, Po-Wei, et al.. (2020). Efficient semidefinite-programming-based inference for binary and multi-class MRFs. arXiv (Cornell University). 33. 4259–4270. 1 indexed citations

8.

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

9.

Wang, Po-Wei & J. Zico Kolter. (2019). Low-Rank Semidefinite Programming for the MAX2SAT Problem. Proceedings of the AAAI Conference on Artificial Intelligence. 33(1). 1641–1649. 2 indexed citations

10.

Wang, Po-Wei, et al.. (2018). Realtime query completion via deep language models. International ACM SIGIR Conference on Research and Development in Information Retrieval. 4 indexed citations

11.

Wang, Po-Wei, et al.. (2016). Epigraph projections for fast general convex programming. International Conference on Machine Learning. 2868–2877. 2 indexed citations

12.

Wang, Po-Wei & Chih‐Jen Lin. (2014). Support Vector Machines.. 187–204. 7 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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