151 citations · 181 across the 9 of their papers we have counts for
9 papers
Gradient Rewiring for Editable Graph Neural Network Training
Zhimeng Jiang, Zirui Liu, Xiaotian Han +6
Deep neural networks are ubiquitously adopted in many applications, such as computer vision, natural language processing, and graph analytics. However, well-trained neural networks…
Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment
Qizhang Feng, Siva Rajesh Kasa, Santhosh Kumar Kasa +3
Large Language Models (LLMs) have seen widespread adoption due to their remarkable natural language capabilities. However, when deploying them in real-world settings, it is importa…
Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A Case Study on Hemorrhagic Stroke
Qizhang Feng, Jiayi Yuan, Forhan Bin Emdad +3
Stroke is a significant cause of mortality and morbidity, necessitating early predictive strategies to minimize risks. Traditional methods for evaluating patients, such as Acute Ph…
Efficient GNN Explanation via Learning Removal-based Attribution
Yao Rong, Guanchu Wang, Qizhang Feng +4
As Graph Neural Networks (GNNs) have been widely used in real-world applications, model explanations are required not only by users but also by legal regulations. However, simultan…
DEGREE: Decomposition Based Explanation For Graph Neural Networks
Qizhang Feng, Ninghao Liu, Fan Yang +3
Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusti…
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang +5
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (N…
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
Ruixiang Tang, Qizhang Feng, Ninghao Liu +2
The huge supporting training data on the Internet has been a key factor in the success of deep learning models. However, this abundance of public-available data also raises concern…
Differentially Private Counterfactuals via Functional Mechanism
Fan Yang, Qizhang Feng, Kaixiong Zhou +2
Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia. Different from the conventional featur…
Fair Machine Learning in Healthcare: A Review
Qizhang Feng, Mengnan Du, Na Zou +1
The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods ca…