Tianshu Yu

Orcid: 0009-0001-9613-5123

Affiliations:
  • Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, China


According to our database1, Tianshu Yu authored at least 13 papers between 2022 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2025
EVA-MILP: Towards Standardized Evaluation of MILP Instance Generation.
CoRR, May, 2025

Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage.
CoRR, May, 2025

Secrets of GFlowNets' Learning Behavior: A Theoretical Study.
CoRR, May, 2025

FullTransNet: Full Transformer with Local-Global Attention for Video Summarization.
CoRR, January, 2025

Diverse AI Feedback For Large Language Model Alignment.
Trans. Assoc. Comput. Linguistics, 2025

Unsupervised Speech-text word-level alignment with Dynamic Programming.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025

HyperPLR: Hypergraph Generation through Projection, Learning, and Reconstruction.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024
Secure and Portable Anonymous Credentials without Tamper-Resistant Hardware.
Proceedings of the 19th Asia Joint Conference on Information Security, 2024

2023
Constructive Large Language Models Alignment with Diverse Feedback.
CoRR, 2023

Speech-Text Dialog Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment.
CoRR, 2023

Reducing Spurious Correlations for Relation Extraction by Feature Decomposition and Semantic Augmentation.
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Speech-Text Pre-training for Spoken Dialog Understanding with Explicit Cross-Modal Alignment.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022
Dependency-aware Prototype Learning for Few-shot Relation Classification.
Proceedings of the 29th International Conference on Computational Linguistics, 2022


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