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cs.AI2026
Open-MMUnlearning: Unifying Methods and Evaluation for MLLM Unlearning
Junkai Chen, Yuhao He, Qianshan Wei +19
As multimodal large language models (MLLMs) become more capable and widely deployed, concerns about privacy and safety have become increasingly pressing. Machine unlearning offers…
cs.AI2026
Fine-Grained Graph Generation through Latent Mixture Scheduling
Nidhi Vakil, Hadi Amiri
Structure aware graph generation aims to generate graphs that satisfy given topological properties. It has applications in domains such as drug discovery, social network modeling,…
cs.AI2023
MultiDelete for Multimodal Machine Unlearning
Jiali Cheng, Hadi Amiri
Machine Unlearning removes specific knowledge about training data samples from an already trained model. It has significant practical benefits, such as purging private, inaccurate,…