@inproceedings{
kang2023nesterov,
title={Nesterov Meets Robust Multitask Learning Twice},
author={Yifan Kang and Kai Liu},
booktitle={OPT 2023: Optimization for Machine Learning},
year={2023},
url={https://openreview.net/forum?id=jp4k4DaSmx}
}
In this paper, we study temporal multitask learning problem where we impose smoothness constraint on time-series weights. Besides, to select important features, group lasso is introduced. Moreover, the regression loss in each time frame is non-squared to alleviate the influence of various scales of noise in each task, in addition to the nuclear norm for low-rank property. We first formulate the objective as a max-min problem, where the dual variable can be optimized via  accelerated dual ascent method, while the primal variable can be solved via \textit{smoothed  Fast Iterative Shrinkage-Thresholding Algorithm} (S-FISTA). We provide convergence analysis of the proposed method and experiments demonstrate its effectiveness.
Nesterov Meets Robust Multitask Learning Twice
Yifan Kang Kai Liu