🧑‍🔬 Yujia Li

🏛️ Affiliations

🎓University of PittsburghPittsburgh · United States2019–2025 · 13 papers🎓University of TorontoToronto · Canada2013–2017 · 12 papers🏢Google DeepMindLondon · United Kingdom2018–2023 · 10 papers🎓Fudan UniversityShanghai · China2010–2025 · 9 papers🔬China Electric Power Research InstituteBeijing2017–2025 · 6 papers🏛️Chinese Academy of SciencesBeijing · China2019–2025 · 6 papers🎓Heidelberg UniversityHeidelberg · Germany2021–2025 · 6 papers🎓Medizinische Fakultät MannheimMannheim2021–2025 · 6 papers🤝University Medical Centre MannheimMannheim · Germany2023–2025 · 5 papers🏢Eli LillyIndianapolis · United States2023–2024 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 43 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Competition-Level Code Generation with AlphaCode (2022 · 1,564 citations)
   AlphaCode addresses the task of writing complete programs for competitive programming problems. In this setting a long natural-language statement must become a correct and fast solution, which is then judged on hidden tests. Earlier large code models handled short, instruction-like tasks but solved only a few percent…
2. Large Language Models as Analogical Reasoners (2023 · 74 citations)
   Chain-of-thought prompting helps LLMs solve multi-step problems, but both common forms have drawbacks. The zero-shot variant gives only generic guidance, and the few-shot variant needs hand-written reasoning demonstrations for every task. Yasunaga et al. (Google DeepMind and Stanford) propose analogical prompting. It…

All works

21 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. Competition-Level Code Generation with AlphaCodeScienceYujia Li, David Choi, Junyoung Chung, et al.2022 · 1,564 citations
2. Scaling Language Models: Methods, Analysis & Insights from Training Gopher ↗arXiv:2112.11446 · no summary yetJack W. Rae et al.2021 · 928 citations
3. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks ↗arXiv (Cornell University) · no summary yetWenjie Luo et al.2017 · 771 citations
4. Gated Graph Sequence Neural Networks ↗arXiv:1511.05493 · no summary yetYujia Li et al.2015 · 576 citations
5. Generative Moment Matching Networks ↗arXiv (Cornell University) · no summary yetYujia Li, Kevin Swersky, Rich Zemel2015 · 560 citations
6. Learning Deep Generative Models of Graphs ↗arXiv (Cornell University) · no summary yetYujia Li et al.2018 · 422 citations
7. Graph Matching Networks for Learning the Similarity of Graph Structured Objects ↗arXiv (Cornell University) · no summary yetYujia Li et al.2019 · 298 citations
8. Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetEdward Choi et al.2020 · 256 citations
9. Imagination-Augmented Agents for Deep Reinforcement Learning ↗arXiv (Cornell University) · no summary yetThéophane Weber et al.2017 · 241 citations
10. Efficient Graph Generation with Graph Recurrent Attention Networks ↗arXiv (Cornell University) · no summary yetRenjie Liao et al.2019 · 170 citations
11. Relational Deep Reinforcement Learning ↗arXiv (Cornell University) · no summary yetVinícius Zambaldi et al.2018 · 160 citations
12. Learning model-based planning from scratch ↗arXiv (Cornell University) · no summary yetRazvan Pascanu et al.2017 · 76 citations
13. Large Language Models as Analogical ReasonersarXiv:2310.01714Michihiro Yasunaga, Xinyun Chen, Yujia Li, et al.2023 · 74 citations
14. Solving Mixed Integer Programs Using Neural Networks ↗arXiv:2012.13349 · no summary yetVinod Nair et al.2020 · 52 citations
15. Exploring Compositional High Order Pattern Potentials for Structured Output Learning ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetYujia Li, Daniel Tarlow, Richard S. Zemel2013 · 33 citations
16. Mean-Field Networks ↗arXiv (Cornell University) · no summary yetYujia Li, Richard S. Zemel2014 · 23 citations
17. WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset ↗Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) · no summary yetLuyu Wang et al.2021 · 22 citations
18. Strong Generalization and Efficiency in Neural Programs ↗arXiv (Cornell University) · no summary yetYujia Li et al.2020 · 22 citations
19. Learning unbiased features ↗arXiv (Cornell University) · no summary yetYujia Li, Kevin Swersky, Richard S. Zemel2014 · 20 citations
20. High Order Regularization for Semi-Supervised Learning of Structured Output Problems ↗no summary yetYujia Li, Rich Zemel2014 · 18 citations
21. Dualing GANs ↗arXiv (Cornell University) · no summary yetYujia Li et al.2017 · 1 citation

🤝 Co-authors here

11 co-authors with a page, the most papers together first.

• Denny Zhou
• Ed H. Chi
• Felix Gimeno
• Julian Schrittwieser
• Junyoung Chung
• Jure Leskovec
• Michihiro Yasunaga
• Nate Kushman
• Panupong Pasupat
• Percy Liang
• Xinyun Chen

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Science: Science 2022
• IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
• Proceedings of the AAAI Conference on Artificial Intelligence
• Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)

🔗 Cited by

Papers of our library cite their works 4,197 times (of the 149,439 papers whose bibliographies we read); 974 citing papers with a page here →.

Updated 2 November 2026

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