254 citations · 1.3k across the 50 of their papers we have counts for
13 papers · 1 filter
Torque-Aware Momentum
Pranshu Malviya, Goncalo Mordido, Aristide Baratin +4
Efficiently exploring complex loss landscapes is key to the performance of deep neural networks. While momentum-based optimizers are widely used in state-of-the-art setups, classic…
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis +3
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is im…
Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method
Teodora Baluta, Pascal Lamblin, Daniel Tarlow +2
Machine unlearning aims to solve the problem of removing the influence of selected training examples from a learned model. Despite the increasing attention to this problem, it rema…
The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse
Ekansh Sharma, Daniel M. Roy, Gintare Karolina Dziugaite
Model merging aims to efficiently combine the weights of multiple expert models, each trained on a specific task, into a single multi-task model, with strong performance across all…
Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization
Phillip Guo, Aaquib Syed, Abhay Sheshadri +2
Methods for knowledge editing and unlearning in large language models seek to edit or remove undesirable knowledge or capabilities without compromising general language modeling pe…
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…