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cs.LG2025
Controllable Machine Unlearning via Gradient Pivoting
Youngsik Hwang, Dong-Young Lim
Machine unlearning (MU) aims to remove the influence of specific data from a trained model. However, approximate unlearning methods, often formulated as a single-objective optimiza…
cs.LG2025
Flatness-Aware Stochastic Gradient Langevin Dynamics
Stefano Bruno, Youngsik Hwang, Jaehyeon An +2
Flatness of the loss landscape has been widely studied as an important perspective for understanding the behavior and generalization of deep learning algorithms. Motivated by this…
stat.ML2025
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization
Youngjun Song, Youngsik Hwang, Jonghun Lee +2
Domain generalization (DG) aims to learn models that perform well on unseen target domains by training on multiple source domains. Sharpness-Aware Minimization (SAM), known for fin…