🧑‍🔬 Yeming Wen

🏛️ Affiliations

🎓University of TorontoToronto · Canada2018–2020 · 6 papers🏢GoogleMountain View · United States2020–2023 · 4 papers🎓The University of Texas at AustinAustin · United States2021–2023 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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. Natural Language to Code Generation in Interactive Data Science Notebooks (2023 · 63 citations)
   Data scientists often work in Jupyter-style notebooks, where each new request to an AI coding assistant depends on earlier cells, their execution state, and previous requests.
2. An In-Context Learning Agent for Formal Theorem-Proving (2023 · 47 citations)
   Formal proof assistants such as Lean and Coq require proofs written as sequences of tactics. The strongest neural provers have so far been trained or finetuned on proof data from one specific system. This paper presents COPRA, an agent that instead relies only on in-context learning. A general-purpose black-box…

All works

8 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. Benchmarking Model-Based Reinforcement Learning ↗arXiv (Cornell University) · no summary yetTingwu Wang et al.2019 · 223 citations
2. Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches ↗arXiv:1803.04386 · no summary yetYeming Wen et al.2018 · 189 citations
3. BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning ↗arXiv (Cornell University) · no summary yetYeming Wen, Dustin Tran, Jimmy Lei Ba2020 · 108 citations
4. Natural Language to Code Generation in Interactive Data Science NotebooksAnnual Meeting of the Association for Computational Linguistics (ACL)Pengcheng Yin, Wen-Ding Li, Kefan Xiao, et al.2023 · 63 citations
5. An In-Context Learning Agent for Formal Theorem-ProvingarXiv:2310.04353Amitayush Thakur, George Tsoukalas, Yeming Wen, et al.2023 · 47 citations
6. Interplay Between Optimization and Generalization of Stochastic Gradient Descent with Covariance Noise. ↗arXiv (Cornell University) · no summary yetYeming Wen et al.2019 · 13 citations
7. An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise ↗International Conference on Artificial Intelligence and Statistics · no summary yetYeming Wen et al.2020 · 7 citations
8. Exploring Curvature Noise in Large-Batch Stochastic Optimization ↗no summary yetYeming Wen et al.2018

🤝 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

• Abhishek Rao
• Amitayush Thakur
• Charles Sutton
• George Tsoukalas
• Henryk Michalewski
• Jimmy Xin
• Joshua Howland
• Kefan Xiao
• Kensen Shi
• Michele Catasta
• Oleksandr Polozov
• Paige Bailey

Venues

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

• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2023
• International Conference on Artificial Intelligence and Statistics

🔗 Cited by

Papers of our library cite their works 135 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated 3 November 2026

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