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 consider the adversarial attack on training data attribution methods.
We design the first efficient machine unlearning evaluation metric with provable guarantees.
