Multi-task and meta-learning with sparse linear bandits

Leonardo Cella, Massimiliano Pontil
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1692-1702, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v161-cella21a, title = {Multi-task and meta-learning with sparse linear bandits}, author = {Cella, Leonardo and Pontil, Massimiliano}, booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence}, pages = {1692--1702}, year = {2021}, editor = {de Campos, Cassio and Maathuis, Marloes H.}, volume = {161}, series = {Proceedings of Machine Learning Research}, month = {27--30 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v161/cella21a/cella21a.pdf}, url = {https://proceedings.mlr.press/v161/cella21a.html}, 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.} }
Endnote
%0 Conference Paper %T Multi-task and meta-learning with sparse linear bandits %A Leonardo Cella %A Massimiliano Pontil %B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2021 %E Cassio de Campos %E Marloes H. Maathuis %F pmlr-v161-cella21a %I PMLR %P 1692--1702 %U https://proceedings.mlr.press/v161/cella21a.html %V 161 %X 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.
APA
Cella, L. & Pontil, M.. (2021). Multi-task and meta-learning with sparse linear bandits. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 161:1692-1702 Available from https://proceedings.mlr.press/v161/cella21a.html.

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