2 papers
cs.LG2026
Exact Unlearning from Proxies Induces Closeness Guarantees on Approximate Unlearning
Virgile Dine, Teddy Furon
This paper proposes a paradigm shift linking machine unlearning directly to the structure of the data distributions rather than a mere update of the neural network parameters. We s…
cs.LG2025
Improving Unlearning with Model Updates Probably Aligned with Gradients
Virgile Dine, Teddy Furon, Charly Faure
We formulate the machine unlearning problem as a general constrained optimization problem. It unifies the first-order methods from the approximate machine unlearning literature. Th…