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cs.LG2025
Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning
Jinseong Park, Mijung Park
Data unlearning aims to remove the influence of specific training samples from a trained model. In fine-tuning methods, data unlearning relies primarily on loss maximization over f…
cs.LG2025
Optimizable Model Averaging for Ensembling Heterogeneous Model Outputs
Mijung Park
Foundation model ensembles can improve accuracy and robustness, but full fine-tuning and parameter-space merging are often expensive or impossible when candidate models have differ…