4 papers
Benchmarking Unlearning for Vision Transformers
Kairan Zhao, Iurie Luca, Peter Triantafillou
Machine unlearning (MU) refers to the post-training capability to remove (the influence of) training examples that are incorrect, biased, or leak sensitive/private information. MU…
Scalability of memorization-based machine unlearning
Kairan Zhao, Peter Triantafillou
Machine unlearning (MUL) focuses on removing the influence of specific subsets of data (such as noisy, poisoned, or privacy-sensitive data) from pretrained models. MUL methods typi…
What makes unlearning hard and what to do about it
Kairan Zhao, Meghdad Kurmanji, George-Octavian BÄrbulescu +2
Machine unlearning is the problem of removing the effect of a subset of training data (the ''forget set'') from a trained model without damaging the model's utility e.g. to comply…
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition
Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa +12
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and initiate discussions on formal and robust…