Debiasing Graph Representation Learning based on Information Bottleneck
Fuente:
arXiv
Saved in:
| Main Authors: | Zhang, Ziyi, Ouyang, Mingxuan, Lin, Wanyu, Lan, Hao, Yang, Lei |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Fairness-Aware Graph Representation Learning with Limited Demographic Information
by: Wang, Zichong, et al.
Published: (2025)
by: Wang, Zichong, et al.
Published: (2025)
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
by: Hong, Jinyung, et al.
Published: (2024)
by: Hong, Jinyung, et al.
Published: (2024)
Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Model
by: Huang, Lin-Chun, et al.
Published: (2025)
by: Huang, Lin-Chun, et al.
Published: (2025)
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax
by: Yao, Hao-Ren, et al.
Published: (2022)
by: Yao, Hao-Ren, et al.
Published: (2022)
On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisions
by: Nguyen, Dang, et al.
Published: (2025)
by: Nguyen, Dang, et al.
Published: (2025)
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
by: Zhu, Yuchang, et al.
Published: (2024)
by: Zhu, Yuchang, et al.
Published: (2024)
Debiasing Machine Learning Models by Using Weakly Supervised Learning
by: Brotto, Renan D. B., et al.
Published: (2024)
by: Brotto, Renan D. B., et al.
Published: (2024)
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications
by: Agarwal, Ankita, et al.
Published: (2024)
by: Agarwal, Ankita, et al.
Published: (2024)
Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education
by: Shao, Pengyang, et al.
Published: (2024)
by: Shao, Pengyang, et al.
Published: (2024)
Interpreting Latent Student Knowledge Representations in Programming Assignments
by: Fernandez, Nigel, et al.
Published: (2024)
by: Fernandez, Nigel, et al.
Published: (2024)
A Multi-LLM Debiasing Framework
by: Owens, Deonna M., et al.
Published: (2024)
by: Owens, Deonna M., et al.
Published: (2024)
Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection
by: Jain, Saachi, et al.
Published: (2024)
by: Jain, Saachi, et al.
Published: (2024)
BoostFGL: Boosting Fairness in Federated Graph Learning
by: Chen, Zekai, et al.
Published: (2026)
by: Chen, Zekai, et al.
Published: (2026)
Learning Interpretable Fair Representations
by: Wang, Tianhao, et al.
Published: (2024)
by: Wang, Tianhao, et al.
Published: (2024)
LLM-Assisted Content Conditional Debiasing for Fair Text Embedding
by: Deng, Wenlong, et al.
Published: (2024)
by: Deng, Wenlong, et al.
Published: (2024)
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
by: Yang, Shangshang, et al.
Published: (2024)
by: Yang, Shangshang, et al.
Published: (2024)
BiasEdit: Debiasing Stereotyped Language Models via Model Editing
by: Xu, Xin, et al.
Published: (2025)
by: Xu, Xin, et al.
Published: (2025)
FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization
by: Yang, Cheng, et al.
Published: (2024)
by: Yang, Cheng, et al.
Published: (2024)
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs
by: Arun, Arvindh, et al.
Published: (2023)
by: Arun, Arvindh, et al.
Published: (2023)
Learning Fair Representations with Kolmogorov-Arnold Networks
by: Priyadarshini, Amisha, et al.
Published: (2025)
by: Priyadarshini, Amisha, et al.
Published: (2025)
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
by: Xiong, Zhang, et al.
Published: (2024)
by: Xiong, Zhang, et al.
Published: (2024)
Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps
by: Sztukiewicz, Lukasz, et al.
Published: (2025)
by: Sztukiewicz, Lukasz, et al.
Published: (2025)
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
by: Jones, Charles, et al.
Published: (2024)
by: Jones, Charles, et al.
Published: (2024)
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes
by: Gallegos, Isabel O., et al.
Published: (2024)
by: Gallegos, Isabel O., et al.
Published: (2024)
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck
by: Shou, Yuntao, et al.
Published: (2024)
by: Shou, Yuntao, et al.
Published: (2024)
The Geometric Price of Discrete Logic: Context-driven Manifold Dynamics of Number Representations
by: Zhang, Long, et al.
Published: (2026)
by: Zhang, Long, et al.
Published: (2026)
Attentive Graph Enhanced Region Representation Learning
by: Chen, Weiliang, et al.
Published: (2023)
by: Chen, Weiliang, et al.
Published: (2023)
Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations
by: Nijdam, Thijmen, et al.
Published: (2024)
by: Nijdam, Thijmen, et al.
Published: (2024)
Leveraging Pedagogical Theories to Understand Student Learning Process with Graph-based Reasonable Knowledge Tracing
by: Cui, Jiajun, et al.
Published: (2024)
by: Cui, Jiajun, et al.
Published: (2024)
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs
by: Ebrahimi, Sana, et al.
Published: (2024)
by: Ebrahimi, Sana, et al.
Published: (2024)
Incorporating Graph Attention Mechanism into Geometric Problem Solving Based on Deep Reinforcement Learning
by: Zhong, Xiuqin, et al.
Published: (2024)
by: Zhong, Xiuqin, et al.
Published: (2024)
Learning Representational Disparities
by: Ravishankar, Pavan, et al.
Published: (2025)
by: Ravishankar, Pavan, et al.
Published: (2025)
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information
by: Iskander, Shadi, et al.
Published: (2024)
by: Iskander, Shadi, et al.
Published: (2024)
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
by: Zhou, Hao, et al.
Published: (2025)
by: Zhou, Hao, et al.
Published: (2025)
Representation Learning of Complex Assemblies, An Effort to Improve Corporate Scope 3 Emissions Calculation
by: Chatterjee, Ajay, et al.
Published: (2024)
by: Chatterjee, Ajay, et al.
Published: (2024)
Latent Representations of Intracardiac Electrograms for Atrial Fibrillation Driver Detection
by: Peiro-Corbacho, Pablo, et al.
Published: (2025)
by: Peiro-Corbacho, Pablo, et al.
Published: (2025)
Unintended Impacts of LLM Alignment on Global Representation
by: Ryan, Michael J., et al.
Published: (2024)
by: Ryan, Michael J., et al.
Published: (2024)
Debiasing surgeon: fantastic weights and how to find them
by: Nahon, Rémi, et al.
Published: (2024)
by: Nahon, Rémi, et al.
Published: (2024)
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
by: Qian, Xiaowei, et al.
Published: (2024)
by: Qian, Xiaowei, et al.
Published: (2024)
Disentangled Representation Learning with Transmitted Information Bottleneck
by: Dang, Zhuohang, et al.
Published: (2023)
by: Dang, Zhuohang, et al.
Published: (2023)
Similar Items
-
Fairness-Aware Graph Representation Learning with Limited Demographic Information
by: Wang, Zichong, et al.
Published: (2025) -
Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks
by: Hong, Jinyung, et al.
Published: (2024) -
Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Model
by: Huang, Lin-Chun, et al.
Published: (2025) -
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax
by: Yao, Hao-Ren, et al.
Published: (2022) -
On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisions
by: Nguyen, Dang, et al.
Published: (2025)