Cella, L;
Pontil, M;
(2021)
Multi-Task and Meta-Learning with Sparse Linear Bandits.
In:
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence.
(pp. pp. 1692-1702).
PMLR
Preview |
Text
cella21a.pdf - Published Version Download (472kB) | Preview |
Abstract
Motivated by recent developments on meta-learning with linear contextual bandit tasks, we study the benefit of feature learning in both the multi-task and meta-learning settings. We focus on the case that the task weight vectors are jointly sparse, i.e. they share the same small set of predictive features. Starting from previous work on standard linear regression with the group-lasso estimator we provide novel oracle-inequalities for this estimator when samples are collected by a bandit policy. Subsequently, building on a recent lasso-bandit policy, we investigate its group-lasso variant and analyze its regret bound. We specialize the proposed policy to the multi-task and meta-learning settings, demonstrating its theoretical advantage. We also point out a deficiency in the state-of-the-art lower bound and observe that our method has a smaller upper bound. Preliminary experiments confirm the effectiveness of our approach in practice.
Type: | Proceedings paper |
---|---|
Title: | Multi-Task and Meta-Learning with Sparse Linear Bandits |
Event: | 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021) |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | https://proceedings.mlr.press/v161/cella21a.html |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
URI: | https://discovery.ucl.ac.uk/id/eprint/10144632 |
Archive Staff Only
![]() |
View Item |