all AI news
Towards Independence Criterion in Machine Unlearning of Features and Labels
March 14, 2024, 4:41 a.m. | Ling Han, Nanqing Luo, Hao Huang, Jing Chen, Mary-Anne Hartley
cs.LG updates on arXiv.org arxiv.org
Abstract: This work delves into the complexities of machine unlearning in the face of distributional shifts, particularly focusing on the challenges posed by non-uniform feature and label removal. With the advent of regulations like the GDPR emphasizing data privacy and the right to be forgotten, machine learning models face the daunting task of unlearning sensitive information without compromising their integrity or performance. Our research introduces a novel approach that leverages influence functions and principles of distributional …
abstract arxiv challenges complexities criterion cs.ai cs.cr cs.lg data data privacy face feature features gdpr labels machine privacy regulations type uniform unlearning work
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Data Analyst (Digital Business Analyst)
@ Activate Interactive Pte Ltd | Singapore, Central Singapore, Singapore