6 citations · 10 across the 6 of their papers we have counts for
6 papers
AI for Biomedicine in the Era of Large Language Models
Zhenyu Bi, Sajib Acharjee Dip, Daniel Hajialigol +4
The capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, pred…
Scaling Team Coordination on Graphs with Reinforcement Learning
Manshi Limbu, Zechen Hu, Xuan Wang +2
This paper studies Reinforcement Learning (RL) techniques to enable team coordination behaviors in graph environments with support actions among teammates to reduce the costs of tr…
XAI-CLASS: Explanation-Enhanced Text Classification with Extremely Weak Supervision
Daniel Hajialigol, Hanwen Liu, Xuan Wang
Text classification aims to effectively categorize documents into pre-defined categories. Traditional methods for text classification often rely on large amounts of manually annota…
PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
Chufan Gao, Xulin Fan, Jimeng Sun +1
Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extra…
Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
Pengcheng Jiang, Shivam Agarwal, Bowen Jin +3
The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarg…
Semi-supervised Transfer Learning for Evaluation of Model Classification Performance
Linshanshan Wang, Xuan Wang, Katherine P. Liao +1
In modern machine learning applications, frequent encounters of covariate shift and label scarcity have posed challenges to robust model training and evaluation. Numerous transfer…