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Nicole Elyse Mitchell

3 accepted papers

2026

Redirection for Erasing Memory (REM): Towards a universal unlearning method for corrupted data

ICLR 2026poster

Machine unlearning is studied for a multitude of tasks, but specialization of unlearning methods to particular tasks has made their systematic comparison challenging. To address this issue, we propose a conceptual space to characterize diverse corrupted data unlearning tasks in vision classifiers. T…

Cited by 0SourceScholar
2025

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research

NeurIPS 2025oral

"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyright, safety, and more. For example, unlearning is often invoked as a solution for removing the effects of specific infor…

Cited by 0SourceScholar
2023

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning

NeurIPS 2023poster

We introduce Dataset Grouper, a library to create large-scale group-structured (e.g., federated) datasets, enabling federated learning simulation at the scale of foundation models. This library facilitates the creation of group-structured versions of existing datasets based on user-specified partiti…