[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" by Chongyu Fan*, Jiancheng Liu*, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu

[NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu

Continual Forgetting for Pre-trained Vision Models (CVPR 2024)

[ACL 2024] Code and data for "Machine Unlearning of Pre-trained Large Language Models"

Official implementation of "Graph Unlearning" (ACM CCS 2022)

Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)

General Strategy for Unlearning in Graph Neural Networks

[ACL 2025] Knowledge Unlearning for Large Language Models

[NeurIPS 2024] Large Language Model Unlearning via Embedding-Corrupted Prompts

"Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning" by Chongyu Fan*, Jiancheng Liu*, Licong Lin*, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu

Code for CVPR22 paper "Deep Unlearning via Randomized Conditionally Independent Hessians"

A federated clustering approach with the corresponding unlearning mechanism (ICLR 2023)

[ECCV24] "Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning" by Chongyu Fan*, Jiancheng Liu*, Alfred Hero, Sijia Liu

Machine Unlearning for Random Forests

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

Certified (approximate) machine unlearning for simplified graph convolutional networks (SGCs) with theoretical guarantees (ICLR 2023)

An Empirical Study of Federated Unlearning: Efficiency and Effectiveness (Accepted Conference Track Papers at ACML 2023)

Official Code of Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers (AAAI 2024)

Official PyTorch Implementation for Continual Learning and Private Unlearning

Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-tuning