3 citations · 4 across the 4 of their papers we have counts for
4 papers
On LLM-Based Scientific Inductive Reasoning Beyond Equations
Brian S. Lin, Jiaxin Yuan, Zihan Zhou +8
As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited e…
Ensuring Safety and Trust: Analyzing the Risks of Large Language Models in Medicine
Yifan Yang, Qiao Jin, Robert Leaman +15
The remarkable capabilities of Large Language Models (LLMs) make them increasingly compelling for adoption in real-world healthcare applications. However, the risks associated with…
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
Xiaoyu Liu, Jiaxin Yuan, Yuhang Zhou +3
The essence of sequential recommender systems (RecSys) lies in understanding how users make decisions. Most existing approaches frame the task as sequential prediction based on use…
C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
Xiaoyu Liu, Jiaxin Yuan, Bang An +3
Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to discover them in the…