Qizhang Feng — accepted AI-conference papers

2025

Exposing Privacy Gaps: Membership Inference Attack on Preference Data for LLM Alignment

Qizhang Feng, Siva Rajesh Kasa, SANTHOSH KUMAR KASA, Hyokun Yun, Choon Hui Teo, Sravan Babu Bodapati

AISTATS 2025poster

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 important to align LLMs to generate texts according to acceptable human standards. Methods such as Proximal Policy Optimization…

Cited by 0 Source ↗Scholar ↗

2024

Gradient Rewiring for Editable Graph Neural Network Training

Zhimeng Jiang, Zirui Liu, Xiaotian Han, Qizhang Feng, Hongye Jin, Qiaoyu Tan +3

NeurIPS 2024poster

Deep neural networks are ubiquitously adopted in many applications, such as computer vision, natural language processing, and graph analytics. However, well-trained neural networks can make prediction errors after deployment as the world changes. \textit{Model editing} involves updating the base mod…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2023

Fair Graph Distillation

Qizhang Feng, Zhimeng Jiang, Ruiquan Li, Yicheng Wang, Na Zou, Jiang Bian +1

NeurIPS 2023poster

As graph neural networks (GNNs) struggle with large-scale graphs due to high computational demands, data distillation for graph data promises to alleviate this issue by distilling a large real graph into a smaller distilled graph while maintaining comparable prediction performance for GNNs trained o…

Cited by 20 Source ↗PDF ↗Scholar ↗

2022

DEGREE: Decomposition Based Explanation for Graph Neural Networks

Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu

ICLR 2022poster

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 trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fa…

Cited by 36 Source ↗PDF ↗code ↗Scholar ↗