3 accepted papers
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under training randomness: the same example may appear critical in one run and irrelevant in the next. Such instability undermi…
The effectiveness of current machine learning models relies on their ability to grasp diverse concepts present in datasets. However, biased and noisy data can inadvertently cause these models to learn certain undesired concepts, undermining their ability to generalize and provide utility. Consequent
To follow regulations on individual data privacy and safety, machine learning models must systematically remove information learned from specific subsets of a user's training data that can no longer be utilized. To address this problem, machine unlearning has emerged as an important area of research…