Web: http://arxiv.org/abs/2111.05968

June 24, 2022, 1:11 a.m. | El Mahdi Chayti, Sai Praneeth Karimireddy, Sebastian U. Stich, Nicolas Flammarion, Martin Jaggi

cs.LG updates on arXiv.org arxiv.org

Collaborative training can improve the accuracy of a model for a user by
trading off the model's bias (introduced by using data from other users who are
potentially different) against its variance (due to the limited amount of data
on any single user). In this work, we formalize the personalized collaborative
learning problem as a stochastic optimization of a task 0 while giving access
to N related but different tasks 1,..., N. We provide convergence guarantees
for two algorithms in …

arxiv collaborative learning lg linear personalized

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY