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

arXiv:2403.08124v1 Announce Type: new
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

Senior Data Engineer

@ Displate | Warsaw

Junior Data Analyst - ESG Data

@ Institutional Shareholder Services | Mumbai

Intern Data Driven Development in Sensor Fusion for Autonomous Driving (f/m/x)

@ BMW Group | Munich, DE

Senior MLOps Engineer, Machine Learning Platform

@ GetYourGuide | Berlin

Data Engineer, Analytics

@ Meta | Menlo Park, CA

Data Engineer

@ Meta | Menlo Park, CA