Publications (13)
Knowledge-aware Attention Network for Protein-Protein Interaction Extraction
Huiwei Zhou, Zhuang Liu1, Shixian Ning +3
Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. However, many of the current PPI extra…
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
Yingyu Lin, Yi-An Ma, Yu-Xiang Wang +2
Posterior sampling, i.e., exponential mechanism to sample from the posterior distribution, provides -pure differential privacy (DP) guarantees and does not suffer from…
Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction
Huiwei Zhou, Chengkun Lang, Zhuang Liu +3
Background: Automatic extraction of chemical-disease relations (CDR) from unstructured text is of essential importance for disease treatment and drug development. Meanwhile, biomed…
On the -Free Inference Complexity of Absorbing Discrete Diffusion
Xunpeng Huang, Yingyu Lin, Nishant Jain +4
Absorbing discrete diffusion has emerged as a dominant framework for discrete data generation. However, a significant disparity remains between its empirical success and theoretica…
Reinforcement Learning without Ground-Truth Solutions can Improve LLMs
Yingyu Lin, Qiyue Gao, Nikki Lijing Kuang +6
Reinforcement learning with verifiable rewards (RLVR) for training LLMs typically rely on ground-truth answers to assign rewards, limiting their applicability to tasks where the gr…
Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge
Huiwei Zhou, Shixian Ning, Yunlong Yang +3
Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases…
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
Yingyu Lin, Yuxing Huang, Wenqin Liu +6
Real-world data often violates the equal-variance assumption (homoscedasticity), making it essential to account for heteroscedastic noise in causal discovery. In this work, we expl…
Almost Linear Convergence under Minimal Score Assumptions: Quantized Transition Diffusion
Xunpeng Huang, Yingyu Lin, Nikki Lijing Kuang +4
Continuous diffusion models have demonstrated remarkable performance in data generation across various domains, yet their efficiency remains constrained by two critical limitations…
Beyond Length: Quantifying Long-Range Information for Long-Context LLM Pretraining Data
Haoran Deng, Yingyu Lin, Zhenghao Lin +4
Long-context language models unlock advanced capabilities in reasoning, code generation, and document summarization by leveraging dependencies across extended spans of text. Howeve…
Leveraging Prior Knowledge for Protein-Protein Interaction Extraction with Memory Network
Huiwei Zhou, Zhuang Liu, Shixian Ning +4
Automatically extracting Protein-Protein Interactions (PPI) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memor…
Residual Skill Optimization for Text-to-SQL Ensembles
Jiongli Zhu, Haoquan Guan, Parjanya Prajakta Prashant +8
Text-to-SQL ensembles improve over single-candidate generation by drawing multiple SQL candidates and selecting one, but their effectiveness is bounded by Pass@K, the probability t…
Purifying Approximate Differential Privacy with Randomized Post-processing
Yingyu Lin, Erchi Wang, Yi-An Ma +1
We propose a framework to convert -approximate Differential Privacy (DP) mechanisms into -pure DP mechanisms under certain conditions, a proce…
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction
Huiwei Zhou, Yunlong Yang, Shixian Ning +4
Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts hav…