6 papers
CryptGNN: Enabling Secure Inference for Graph Neural Networks
Pritam Sen, Yao Ma, Cristian Borcea
We present CryptGNN, a secure and effective inference solution for third-party graph neural network (GNN) models in the cloud, which are accessed by clients as ML as a service (MLa…
Knowledge Homophily in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar +6
Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking.…
Modeling Insider Filing Delays in Financial Markets with an Interpretable XGBoost Framework
Cheng Huang, Yao Ma, Fan Gao +10
Timely disclosure of insider transactions is a cornerstone of market transparency, yet delays in filing remain widespread and challenging to monitor at scale. This study introduces…
A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Yongjia Lei +6
Large language models have been extensively studied as neural knowledge bases for their knowledge access, editability, reasoning, and explainability. However, few works focus on th…
Gradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation
Yao Ma, Samuel Louvan, Zhunxuan Wang
Multi-source unsupervised domain adaptation aims to leverage labeled data from multiple source domains for training a machine learning model to generalize well on a target domain w…
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation
Nithish Kannen, Yao Ma, Gerrit J. J. van den Burg +1
News recommendation is a challenging task that involves personalization based on the interaction history and preferences of each user. Recent works have leveraged the power of pret…