Search Results for author: Dongliang Guo

Found 8 papers, 3 papers with code

Ensemble of Large Language Models for Curated Labeling and Rating of Free-text Data

1 code implementation14 Jan 2025 Jiaxing Qiu, Dongliang Guo, Papini Natalie, Peace Noelle, Levinson Cheri, Teague R. Henry

Free-text responses are commonly collected in psychological studies, providing rich qualitative insights that quantitative measures may not capture.

Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing

no code implementations23 Oct 2024 Dongliang Guo, Mengxuan Hu, Zihan Guan, Junfeng Guo, Thomas Hartvigsen, Sheng Li

Through empirical studies on the capability for performing backdoor attack in large pre-trained models ($\textit{e. g.,}$ ViT), we find the following unique challenges of attacking large pre-trained models: 1) the inability to manipulate or even access large training datasets, and 2) the substantial computational resources required for training or fine-tuning these models.

Backdoor Attack Image Captioning +3

Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks

no code implementations26 Apr 2024 Duna Zhan, Dongliang Guo, Pengsheng Ji, Sheng Li

FairGI employs the similarity matrix of individuals to achieve individual fairness within groups, while leveraging adversarial learning to address group fairness in terms of both Equal Opportunity and Statistical Parity.

Drug Discovery Fairness +2

XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications

no code implementations11 Dec 2023 Zhongliang Zhou, Mengxuan Hu, Mariah Salcedo, Nathan Gravel, Wayland Yeung, Aarya Venkat, Dongliang Guo, Jielu Zhang, Natarajan Kannan, Sheng Li

Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data.

Navigate

Trustworthy Representation Learning Across Domains

no code implementations23 Aug 2023 Ronghang Zhu, Dongliang Guo, Daiqing Qi, Zhixuan Chu, Xiang Yu, Sheng Li

Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i. e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction.

Fairness Representation Learning

Fair Attribute Completion on Graph with Missing Attributes

1 code implementation25 Feb 2023 Dongliang Guo, Zhixuan Chu, Sheng Li

To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems.

Attribute Fairness +1

Explainable Anomaly Detection in Images and Videos: A Survey

1 code implementation13 Feb 2023 Yizhou Wang, Dongliang Guo, Sheng Li, Octavia Camps, Yun Fu

This paper provides the first survey concentrated on explainable visual anomaly detection methods.

Anomaly Detection Survey

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