Ga Wu

414 total citations
17 papers, 193 citations indexed

About

Ga Wu is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Ga Wu has authored 17 papers receiving a total of 193 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 10 papers in Information Systems and 5 papers in Management Science and Operations Research. Recurrent topics in Ga Wu's work include Recommender Systems and Techniques (9 papers), Topic Modeling (5 papers) and Sentiment Analysis and Opinion Mining (4 papers). Ga Wu is often cited by papers focused on Recommender Systems and Techniques (9 papers), Topic Modeling (5 papers) and Sentiment Analysis and Opinion Mining (4 papers). Ga Wu collaborates with scholars based in Canada, Australia and Austria. Ga Wu's co-authors include Scott Sanner, Kai Luo, Hojin Yang, Maksims Volkovs, Himanshu Rai, Masoud Hashemi, Harold Soh, Mohamed Reda Bouadjenek, Yu Zhou and Yichao Lu and has published in prestigious journals such as Machine Learning, ACM Transactions on the Web and arXiv (Cornell University).

In The Last Decade

Ga Wu

16 papers receiving 186 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ga Wu Canada 9 141 110 39 34 15 17 193
Sheng Zhou China 9 141 1.0× 63 0.6× 46 1.2× 23 0.7× 11 0.7× 24 195
Xinyu Lin China 8 174 1.2× 184 1.7× 75 1.9× 39 1.1× 13 0.9× 17 276
Huobin Tan China 4 214 1.5× 201 1.8× 34 0.9× 18 0.5× 6 0.4× 20 280
Cheol-Young Ock South Korea 9 219 1.6× 55 0.5× 29 0.7× 10 0.3× 6 0.4× 38 260
Felice Antonio Merra Italy 8 191 1.4× 149 1.4× 87 2.2× 41 1.2× 21 1.4× 19 266
Jingyun Xu China 9 302 2.1× 53 0.5× 31 0.8× 46 1.4× 9 0.6× 19 352
Teng Xiao China 9 182 1.3× 176 1.6× 47 1.2× 66 1.9× 8 0.5× 17 259
Sehee Chung South Korea 4 181 1.3× 186 1.7× 50 1.3× 66 1.9× 11 0.7× 6 245
Iwan Tri Riyadi Yanto Indonesia 9 97 0.7× 100 0.9× 15 0.4× 39 1.1× 15 1.0× 42 195
Yuxian Gu China 6 259 1.8× 44 0.4× 72 1.8× 18 0.5× 15 1.0× 9 311

Countries citing papers authored by Ga Wu

Since Specialization
Citations

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

Fields of papers citing papers by Ga Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ga Wu. 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 Ga Wu. The network helps show where Ga Wu may publish in the future.

Co-authorship network of co-authors of Ga Wu

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

All Works

17 of 17 papers shown
1.
Bouadjenek, Mohamed Reda, Scott Sanner, & Ga Wu. (2022). A User-Centric Analysis of Social Media for Stock Market Prediction. ACM Transactions on the Web. 17(2). 1–22. 7 indexed citations
2.
Wu, Ga, et al.. (2022). PUMA: Performance Unchanged Model Augmentation for Training Data Removal. Proceedings of the AAAI Conference on Artificial Intelligence. 36(8). 8675–8682. 21 indexed citations
3.
Mai, Zheda, et al.. (2022). Distributional Contrastive Embedding for Clarification-based Conversational Critiquing. Proceedings of the ACM Web Conference 2022. 2422–2432. 1 indexed citations
4.
Wu, Ga, Justin Domke, & Scott Sanner. (2022). Arbitrary conditional inference in variational autoencoders via fast prior network training. Machine Learning. 111(7). 2537–2559. 1 indexed citations
5.
Sui, Yi, Ga Wu, & Scott Sanner. (2021). Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models. Neural Information Processing Systems. 34. 1 indexed citations
6.
7.
Luo, Kai, et al.. (2020). Latent Linear Critiquing for Conversational Recommender Systems. 2535–2541. 24 indexed citations
8.
Mai, Zheda, Ga Wu, Kai Luo, & Scott Sanner. (2020). Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering. 165–172.
9.
Luo, Kai, Hojin Yang, Ga Wu, & Scott Sanner. (2020). Deep Critiquing for VAE-based Recommender Systems. 1269–1278. 22 indexed citations
10.
11.
Wu, Ga, et al.. (2019). Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models.. arXiv (Cornell University). 2 indexed citations
12.
Wu, Ga, et al.. (2019). Noise Contrastive Estimation for One-Class Collaborative Filtering. 135–144. 26 indexed citations
13.
Wu, Ga, Kai Luo, Scott Sanner, & Harold Soh. (2019). Deep language-based critiquing for recommender systems. 137–145. 26 indexed citations
14.
Wu, Ga, Mohamed Reda Bouadjenek, & Scott Sanner. (2019). One-Class Collaborative Filtering with the Queryable Variational Autoencoder. 921–924. 8 indexed citations
15.
Volkovs, Maksims, et al.. (2018). Two-stage Model for Automatic Playlist Continuation at Scale. 1–6. 20 indexed citations
16.
17.
Wu, Ga, et al.. (2015). Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time. Proceedings of the AAAI Conference on Artificial Intelligence. 29(1). 3 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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