We consider the adversarial attack on training data attribution methods.
We developed a efficient library for data attribution, aiming to streamline the development of data attribution algorithms.
We provide a comprehensive study of the common practices in the Most Influential Subset Selection (MISS) problem.
We propose a new non-linear data augmentation framework powered by information geometry.
We design the first efficient machine unlearning evaluation metric with provable guarantees.
Exploring a novel approach to exactly solve an NP-hard combinatorial optimization problem by using imitation learning.
