Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis

Vidyashankar Sivakumar, Steven Wu, Arindam Banerjee
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9026-9035, 2020.

Abstract

Bandit learning algorithms typically involve the balance of exploration and exploitation. However, in many practical applications, worst-case scenarios needing systematic exploration are seldom encountered. In this work, we consider a smoothed setting for structured linear contextual bandits where the adversarial contexts are perturbed by Gaussian noise and the unknown parameter θ has structure, e.g., sparsity, group sparsity, low rank, etc. We propose simple greedy algorithms for both the single- and multi-parameter (i.e., different parameter for each context) settings and provide a unified regret analysis for θ with any assumed structure. The regret bounds are expressed in terms of geometric quantities such as Gaussian widths associated with the structure of θ. We also obtain sharper regret bounds compared to earlier work for the unstructured θ setting as a consequence of our improved analysis. We show there is implicit exploration in the smoothed setting where a simple greedy algorithm works.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-sivakumar20a, title = {Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis}, author = {Sivakumar, Vidyashankar and Wu, Steven and Banerjee, Arindam}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {9026--9035}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/sivakumar20a/sivakumar20a.pdf}, url = {https://proceedings.mlr.press/v119/sivakumar20a.html}, abstract = {Bandit learning algorithms typically involve the balance of exploration and exploitation. However, in many practical applications, worst-case scenarios needing systematic exploration are seldom encountered. In this work, we consider a smoothed setting for structured linear contextual bandits where the adversarial contexts are perturbed by Gaussian noise and the unknown parameter $\theta^*$ has structure, e.g., sparsity, group sparsity, low rank, etc. We propose simple greedy algorithms for both the single- and multi-parameter (i.e., different parameter for each context) settings and provide a unified regret analysis for $\theta^*$ with any assumed structure. The regret bounds are expressed in terms of geometric quantities such as Gaussian widths associated with the structure of $\theta^*$. We also obtain sharper regret bounds compared to earlier work for the unstructured $\theta^*$ setting as a consequence of our improved analysis. We show there is implicit exploration in the smoothed setting where a simple greedy algorithm works.} }
Endnote
%0 Conference Paper %T Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis %A Vidyashankar Sivakumar %A Steven Wu %A Arindam Banerjee %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-sivakumar20a %I PMLR %P 9026--9035 %U https://proceedings.mlr.press/v119/sivakumar20a.html %V 119 %X Bandit learning algorithms typically involve the balance of exploration and exploitation. However, in many practical applications, worst-case scenarios needing systematic exploration are seldom encountered. In this work, we consider a smoothed setting for structured linear contextual bandits where the adversarial contexts are perturbed by Gaussian noise and the unknown parameter $\theta^*$ has structure, e.g., sparsity, group sparsity, low rank, etc. We propose simple greedy algorithms for both the single- and multi-parameter (i.e., different parameter for each context) settings and provide a unified regret analysis for $\theta^*$ with any assumed structure. The regret bounds are expressed in terms of geometric quantities such as Gaussian widths associated with the structure of $\theta^*$. We also obtain sharper regret bounds compared to earlier work for the unstructured $\theta^*$ setting as a consequence of our improved analysis. We show there is implicit exploration in the smoothed setting where a simple greedy algorithm works.
APA
Sivakumar, V., Wu, S. & Banerjee, A.. (2020). Structured Linear Contextual Bandits: A Sharp and Geometric Smoothed Analysis. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:9026-9035 Available from https://proceedings.mlr.press/v119/sivakumar20a.html.

Related Material