2 citations · 6 across the 16 of their papers we have counts for
4 papers · 1 filter
Forgetting-MarI: LLM Unlearning via Marginal Information Regularization
Shizhou Xu, Yuan Ni, Stefan Broecker +1
As AI models are trained on ever-expanding datasets, the ability to remove the influence of specific data from trained models has become essential for privacy protection and regula…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration
Xue Feng, M. Paul Laiu, Thomas Strohmer
Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data…
Machine Unlearning via Information Theoretic Regularization
Shizhou Xu, Thomas Strohmer
How can we effectively remove or ``unlearn'' undesirable information, such as specific features or the influence of individual data points, from a learning outcome while minimizing…