Cover Image

Exploring Fairness in Educational Data Mining in the Context of the Right to be Forgotten

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Qian, Wei, Chen, Aobo, Zhao, Chenxu, Li, Yangyi, Huai, Mengdi
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgotten
by: Qian, Wei, et al.
Published: (2025)

Towards Benchmarking Privacy Vulnerabilities in Selective Forgetting with Large Language Models
by: Qian, Wei, et al.
Published: (2025)

Membership Inference Attacks with False Discovery Rate Control
by: Zhao, Chenxu, et al.
Published: (2025)

Quantifying and Understanding Uncertainty in Large Reasoning Models
by: Li, Yangyi, et al.
Published: (2026)

Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
by: Chen, Aobo, et al.
Published: (2026)

Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction
by: Qian, Wei, et al.
Published: (2024)

Uncertainty-aware Language Guidance for Concept Bottleneck Models
by: Li, Yangyi, et al.
Published: (2026)

Quantifying Uncertainty in Natural Language Explanations of Large Language Models for Question Answering
by: Li, Yangyi, et al.
Published: (2025)

A survey of security and privacy issues of machine unlearning
by: Aobo Chen, et al.
Published: (2025)

The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Models
by: Xiao, Yang, et al.
Published: (2025)

Enable the Right to be Forgotten with Federated Client Unlearning in Medical Imaging
by: Deng, Zhipeng, et al.
Published: (2024)

Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
by: Jiang, Yu, et al.
Published: (2025)

Data Heterogeneity and Forgotten Labels in Split Federated Learning
by: Tirana, Joana, et al.
Published: (2025)

An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items
by: Corbucci, Luca, et al.
Published: (2025)

Mind the Gap: Data Rewriting for Stable Off-Policy Supervised Fine-Tuning
by: Zhao, Shiwan, et al.
Published: (2025)

From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models
by: Yang, Yuncong, et al.
Published: (2024)

An Effective Software Risk Prediction Management Analysis of Data Using Machine Learning and Data Mining Method
by: Xu, Jinxin, et al.
Published: (2024)

What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests
by: Staufer, Dimitri
Published: (2025)

Not All Tokens Are Meant to Be Forgotten
by: Zhou, Xiangyu, et al.
Published: (2025)

A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining
by: Lin, Yuanguo, et al.
Published: (2023)

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
by: Xiong, Zhang, et al.
Published: (2024)

Towards Cross-Table Masked Pretraining for Web Data Mining
by: Ye, Chao, et al.
Published: (2023)

Unlearning at Scale: Implementing the Right to be Forgotten in Large Language Models
by: X, Abdullah
Published: (2025)

Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity
by: Zhang, Zixuan, et al.
Published: (2026)

Temporal Sampling for Forgotten Reasoning in LLMs
by: Li, Yuetai, et al.
Published: (2025)

Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning
by: Grimes, Keltin, et al.
Published: (2024)

Fair In-Context Learning via Latent Concept Variables
by: Bhaila, Karuna, et al.
Published: (2024)

WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting
by: Liang, Aobo, et al.
Published: (2024)

Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines
by: Liang, Aobo, et al.
Published: (2025)

Improving LLM Group Fairness on Tabular Data via In-Context Learning
by: Cherepanova, Valeriia, et al.
Published: (2024)

How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
by: Small, Edward, et al.
Published: (2022)

Testing for Causal Fairness
by: Fu, Jiarun, et al.
Published: (2025)

Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
by: Li, Yikuan, et al.
Published: (2023)

Generative Data Mining with Longtail-Guided Diffusion
by: Hayden, David S., et al.
Published: (2025)

A Boolean Function-Theoretic Framework for Expressivity in GNNs with Applications to Fair Graph Mining
by: Pal, Manjish
Published: (2026)

MCRAGE: Synthetic Healthcare Data for Fairness
by: Behal, Keira, et al.
Published: (2023)

Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
by: Wu, Yuchen, et al.
Published: (2024)

FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications
by: Yin, Zhipeng, et al.
Published: (2024)

The Forgotten Shield: Safety Grafting in Parameter-Space for Medical MLLMs
by: Zhao, Jiale, et al.
Published: (2025)

AFed: Algorithmic Fair Federated Learning
by: Chen, Huiqiang, et al.
Published: (2025)