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cs.LG2026
SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei +3
Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous knowledge, without costly full…
cs.LG2025
Multi-modal Synthetic Data Training and Model Collapse: Insights from VLMs and Diffusion Models
Zizhao Hu, Mohammad Rostami, Jesse Thomason
Recent research has highlighted the risk of generative model collapse, where performance progressively degrades when continually trained on self-generated data. However, existing e…
cs.LG2025
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning
Amin Banayeeanzade, Mahdi Soltanolkotabi, Mohammad Rostami
Multi-task learning (MTL) is a machine learning paradigm that aims to improve the generalization performance of a model on multiple related tasks by training it simultaneously on t…