On regret bounds for continual single-index learning
Machine Learning
2022-08-26 v2 Machine Learning
Abstract
In this paper, we generalize the problem of single-index model to the context of continual learning in which a learner is challenged with a sequence of tasks one by one and the dataset of each task is revealed in an online fashion. We propose a randomized strategy that is able to learn a common single-index (meta-parameter) for all tasks and a specific link function for each task. The common single-index allows to transfer the information gained from the previous tasks to a new one. We provide a rigorous theoretical analysis of our proposed strategy by proving some regret bounds under different assumption on the loss function.
Cite
@article{arxiv.2102.12961,
title = {On regret bounds for continual single-index learning},
author = {The Tien Mai},
journal= {arXiv preprint arXiv:2102.12961},
year = {2022}
}