James Tin-Yau Kwok
James Tin-Yau Kwok has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Jiahao Huo, Shuliang Liu, Xin Zou.
OpenAlex · ORCID · Wikidata · h-index 63 · 353 works · 21,750 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 697 times in our library · 9 more in our library without a page yet · ⭐ Paper Stars ranking: #3589
Affiliations
Fields, topics and concepts
📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 1 workArts and Humanities · 1 workComputational Theory and Mathematics · 1 workLanguage and Linguistics · 1 workMathematics · 1 workComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work
🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 workInformation retrieval & search · 1 work
Works by year
2020s
Papers
- MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models (2023 · 565 citations)
Open-source LLMs such as LLaMA-2 lag well behind closed models on multi-step maths word problems. Fine-tuning helps, but it depends on having enough varied training questions. MetaMath addresses this by growing the GSM8K and MATH training sets from the question side as well as the answer side, producing a dataset… - Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework (2026 · 2 citations)
Multi-vector models for Visual Document Retrieval (VDR), such as those in the ColPali family, represent each page as hundreds or thousands of patch embeddings.
All works
- Generalizing from a Few Examples ↗ACM Computing Surveys · no summary yetYaqing Wang et al.2020 · 2,778 citations
- MetaMath: Bootstrap Your Own Mathematical Questions for Large Language ModelsarXiv:2309.12284Longhui Yu, Weisen Jiang, Han Shi, et al.2023 · 565 citations
- KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion ↗Findings of the Association for Computational Linguistics: EMNLP 2023 · no summary yetYanbin Wei et al.2023 · 118 citations
- Semi-supervised learning using label mean ↗International Conference on Machine Learning · no summary yetYu-Feng Li, James Tin-Yau Kwok, Zhi‐Hua Zhou2009 · 109 citations
- PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis ↗arXiv:2310.00426 · no summary yetJunsong Chen et al.2023 · 87 citations
- A regularization framework for multiple-instance learning ↗International Conference on Machine Learning · no summary yetPak-Ming Cheung, James Tin-Yau Kwok2006 · 81 citations
- Efficient Learning of Timeseries Shapelets ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetLu Hou, James Tin-Yau Kwok, Jacek M. Żurada2016 · 75 citations
- Time Series Anomaly Detection with Multiresolution Ensemble Decoding ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetLifeng Shen et al.2021 · 56 citations
- Forward-Backward Reasoning in Large Language Models for Mathematical Verification ↗Findings of the Association for Computational Linguistics: ACL 2024 · no summary yetWeisen Jiang et al.2024 · 44 citations
- Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge FrameworkFindings of the Association for Computational LinguisticsYibo Yan, Mingdong Ou, Yi Cao, et al.2026 · 2 citations
- Diffusion with Truncated Blocks: Fast and High-Quality Text Generation using Truncated Block Generation ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYuyan Zhou, Weiyu Chen, James Kwok2026
Co-authors here
Venues
- Findings of the Association for Computational Linguistics: ACL 2024 ×2
- International Conference on Machine Learning ×2
- Proceedings of the AAAI Conference on Artificial Intelligence ×2
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- ACM Computing Surveys
- Findings of the Association for Computational Linguistics: EMNLP 2023
Cited by
Updated
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