Liuyi Yao

1.2k total citations · 1 hit paper
27 papers, 644 citations indexed

About

Liuyi Yao is a scholar working on Artificial Intelligence, Statistics and Probability and Cognitive Neuroscience. According to data from OpenAlex, Liuyi Yao has authored 27 papers receiving a total of 644 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 7 papers in Statistics and Probability and 2 papers in Cognitive Neuroscience. Recurrent topics in Liuyi Yao's work include Advanced Causal Inference Techniques (7 papers), Privacy-Preserving Technologies in Data (6 papers) and Statistical Methods and Inference (5 papers). Liuyi Yao is often cited by papers focused on Advanced Causal Inference Techniques (7 papers), Privacy-Preserving Technologies in Data (6 papers) and Statistical Methods and Inference (5 papers). Liuyi Yao collaborates with scholars based in United States, China and Hong Kong. Liuyi Yao's co-authors include Yaliang Li, Aidong Zhang, Sheng Li, Jing Gao, Zhixuan Chu, Mengdi Huai, Bolin Ding, Jingren Zhou, Yuexiang Xie and Daoyuan Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Knowledge and Data Engineering and Proceedings of the VLDB Endowment.

In The Last Decade

Liuyi Yao

23 papers receiving 626 citations

Hit Papers

align trajectories

What are hit papers?

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.

A Survey on Causal Inference

2021 257 citations Liuyi Yao, Zhixuan Chu et al. ACM Transactions on Knowledge Discovery from Data profile →

Peers

Liuyi Yao

AI

SP

IS

MSOR

CVPR

David Mease United States

Boris Kovalerchuk United States

Francisco Javier Díez Spain

Sławomir T. Wierzchoń Poland

Zhixuan Chu United States

Ori Stitelman United States

Lixin Cui China

Jorma Laurikkala Finland

Zahid Khan Pakistan

David Mease United States

Liuyi Yao

298 ×0.8

AI

60 ×0.5

SP

100 ×1.1

IS

37 ×0.7

MSOR

83 ×1.8

CVPR

Citations per year, relative to Liuyi Yao Liuyi Yao (= 1×) peers David Mease

Countries citing papers authored by Liuyi Yao

Since Specialization

Citations

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

Fields of papers citing papers by Liuyi Yao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liuyi Yao

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

20 of 20 papers shown

1.

Cui, Yue, Liuyi Yao, Weijie Shi, et al.. (2025). Enhancing Tool Learning in Large Language Models with Hierarchical Error Checklists. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 16357–16375.

2.

Qin, Zhen, Daoyuan Chen, Wenhao Zhang, et al.. (2025). The Synergy Between Data and Multi-Modal Large Language Models: A Survey From Co-Development Perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(10). 8415–8434. 1 indexed citations

3.

Chen, Daoyuan, et al.. (2024). On the Convergence of Zeroth-Order Federated Tuning for Large Language Models. 1827–1838. 9 indexed citations

4.

Yao, Liuyi, et al.. (2024). Is Sharing Neighbor Generator in Federated Graph Learning Safe?. IEEE Transactions on Knowledge and Data Engineering. 36(12). 8568–8579.

5.

Yao, Liuyi, et al.. (2024). Gastrointestinal health anti-diarrheal mixture relieves spleen deficiency-induced diarrhea through regulating gut microbiota. Open Life Sciences. 19(1). 20220964–20220964.

6.

Li, Zitao, Bolin Ding, Liuyi Yao, et al.. (2024). Performance-Based Pricing for Federated Learning via Auction. Proceedings of the VLDB Endowment. 17(6). 1269–1282. 5 indexed citations

7.

Yao, Liuyi, et al.. (2023). Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 4743–4755. 13 indexed citations

8.

Xie, Yuexiang, Zhen Wang, Dawei Gao, et al.. (2023). FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. Proceedings of the VLDB Endowment. 16(5). 1059–1072. 50 indexed citations

9.

Yao, Liuyi, Yaliang Li, Sheng Li, et al.. (2022). Concept-Level Model Interpretation From the Causal Aspect. IEEE Transactions on Knowledge and Data Engineering. 35(9). 8799–8810. 5 indexed citations

10.

Huai, Mengdi, Tianhang Zheng, Chenglin Miao, Liuyi Yao, & Aidong Zhang. (2022). On the Robustness of Metric Learning: An Adversarial Perspective. ACM Transactions on Knowledge Discovery from Data. 16(5). 1–25. 5 indexed citations

11.

Yao, Liuyi, Zhixuan Chu, Sheng Li, et al.. (2021). A Survey on Causal Inference. ACM Transactions on Knowledge Discovery from Data. 15(5). 1–46. 257 indexed citations breakdown →

12.

Zhang, Hanbin, Liuyi Yao, Huining Li, et al.. (2021). MSLife. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 5(4). 1–35. 12 indexed citations

13.

Yao, Liuyi, Yaliang Li, Sheng Li, et al.. (2021). SCI. 3583–3587. 5 indexed citations

14.

Yao, Liuyi, Shanlei Mu, Wayne Xin Zhao, et al.. (2021). Debiasing Learning based Cross-domain Recommendation. 3190–3199. 19 indexed citations

15.

Cui, Peng, Zheyan Shen, Sheng Li, et al.. (2020). Causal Inference Meets Machine Learning. 3527–3528. 29 indexed citations

16.

Liu, Jinduo, Junzhong Ji, Guangxu Xun, et al.. (2020). EC-GAN: Inferring Brain Effective Connectivity via Generative Adversarial Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 34(4). 4852–4859. 25 indexed citations

17.

Huai, Mengdi, Hongfei Xue, Chenglin Miao, et al.. (2019). Deep Metric Learning: The Generalization Analysis and an Adaptive Algorithm. 2535–2541. 14 indexed citations

18.

Yao, Liuyi, Sheng Li, Yaliang Li, et al.. (2019). ACE: Adaptively Similarity-Preserved Representation Learning for Individual Treatment Effect Estimation. 1432–1437. 9 indexed citations

19.

Yao, Liuyi, Sheng Li, Yaliang Li, et al.. (2019). On the Estimation of Treatment Effect with Text Covariates. 4106–4113. 14 indexed citations

20.

Yao, Liuyi, Sheng Li, Yaliang Li, et al.. (2018). Representation Learning for Treatment Effect Estimation from Observational Data. Neural Information Processing Systems. 31. 2633–2643. 66 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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