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Han Guo

Han Guo has 2 papers in our library, in 2022–2024; often with Cheng-Ping Hsieh, Ekin Akyürek, Eric P. Xing.

2 papers in our library · 2022–2024

OpenAlex · ORCID · h-index 14 · 88 works · 961 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 206 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5157

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of California San DiegoSan Diego · United States2022–2026 · 8 paperscurrent🎓Northwest A&F UniversityYangling · China2022–2026 · 2 paperscurrent🏢China TelecomBeijing · China2022–2024 · 5 papers🏢China TelecomChangsha · China2022 · 4 papers🎓Zhejiang UniversityHangzhou · China2018–2021 · 4 papers🎓Kyoto UniversityKyoto · Japan2023–2025 · 3 papers🏢TencentShenzhen · China2022–2023 · 3 papers🎓Carnegie Mellon UniversityPittsburgh · United States2022 · 3 papers🔬Laboratoire RobervalCompiègne · France2020–2022 · 3 papers🏢State Grid Corporation of ChinaBeijing · China2020–2021 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 31 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.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

 Works by year

2020s

20241 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning (2022 · 338 citations)
    The paper addresses the challenge of automatically finding effective discrete text prompts for frozen pre-trained language models.
  2. The Surprising Effectiveness of Test-Time Training for Few-Shot Learning (2024 · 36 citations)
    Language models handle tasks similar to their training data well. However, on structurally novel problems they often fail, even when the prompt contains a few examples. The authors, from MIT, investigate test-time training (TTT). During inference, the model is temporarily fine-tuned through adapters, using data…

All works

3 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. RLPrompt: Optimizing Discrete Text Prompts with Reinforcement LearningConference on Empirical Methods in Natural Language Processing (EMNLP)Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.2022 · 338 citations
  2. The Surprising Effectiveness of Test-Time Training for Few-Shot LearningarXiv:2411.07279Ekin Akyürek, Mehul Damani, Adam Zweiger, et al.2024 · 36 citations
  3. Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach ↗arXiv (Cornell University) · no summary yetHan Wen Guo et al.2023 · 1 citation

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 206 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated

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