Linear bandits with polylogarithmic minimax regret

Josep Lumbreras, Marco Tomamiche
Proceedings of Thirty Seventh Conference on Learning Theory, PMLR 247:3644-3682, 2024.

Abstract

We study a noise model for linear stochastic bandits for which the subgaussian noise parameter vanishes linearly as we select actions on the unit sphere closer and closer to the unknown vector. We introduce an algorithm for this problem that exhibits a minimax regret scaling as $\log^3(T)$ in the time horizon $T$, in stark contrast the square root scaling of this regret for typical bandit algorithms. Our strategy, based on weighted least-squares estimation, achieves the eigenvalue relation $\lambda_{\min} ( V_t ) = \Omega (\sqrt{\lambda_{\max}(V_t ) })$ for the design matrix $V_t$ at each time step $t$ through geometrical arguments that are independent of the noise model and might be of independent interest. This allows us to tightly control the expected regret in each time step to be of the order $O(\frac1{t})$, leading to the logarithmic scaling of the cumulative regret.

Cite this Paper

BibTeX
@InProceedings{pmlr-v247-lumbreras24a, title = {Linear bandits with polylogarithmic minimax regret}, author = {Lumbreras, Josep and Tomamiche, Marco}, booktitle = {Proceedings of Thirty Seventh Conference on Learning Theory}, pages = {3644--3682}, year = {2024}, editor = {Agrawal, Shipra and Roth, Aaron}, volume = {247}, series = {Proceedings of Machine Learning Research}, month = {30 Jun--03 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v247/lumbreras24a/lumbreras24a.pdf}, url = {https://proceedings.mlr.press/v247/lumbreras24a.html}, abstract = { We study a noise model for linear stochastic bandits for which the subgaussian noise parameter vanishes linearly as we select actions on the unit sphere closer and closer to the unknown vector. We introduce an algorithm for this problem that exhibits a minimax regret scaling as $\log^3(T)$ in the time horizon $T$, in stark contrast the square root scaling of this regret for typical bandit algorithms. Our strategy, based on weighted least-squares estimation, achieves the eigenvalue relation $\lambda_{\min} ( V_t ) = \Omega (\sqrt{\lambda_{\max}(V_t ) })$ for the design matrix $V_t$ at each time step $t$ through geometrical arguments that are independent of the noise model and might be of independent interest. This allows us to tightly control the expected regret in each time step to be of the order $O(\frac1{t})$, leading to the logarithmic scaling of the cumulative regret.} }
Endnote
%0 Conference Paper %T Linear bandits with polylogarithmic minimax regret %A Josep Lumbreras %A Marco Tomamiche %B Proceedings of Thirty Seventh Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2024 %E Shipra Agrawal %E Aaron Roth %F pmlr-v247-lumbreras24a %I PMLR %P 3644--3682 %U https://proceedings.mlr.press/v247/lumbreras24a.html %V 247 %X We study a noise model for linear stochastic bandits for which the subgaussian noise parameter vanishes linearly as we select actions on the unit sphere closer and closer to the unknown vector. We introduce an algorithm for this problem that exhibits a minimax regret scaling as $\log^3(T)$ in the time horizon $T$, in stark contrast the square root scaling of this regret for typical bandit algorithms. Our strategy, based on weighted least-squares estimation, achieves the eigenvalue relation $\lambda_{\min} ( V_t ) = \Omega (\sqrt{\lambda_{\max}(V_t ) })$ for the design matrix $V_t$ at each time step $t$ through geometrical arguments that are independent of the noise model and might be of independent interest. This allows us to tightly control the expected regret in each time step to be of the order $O(\frac1{t})$, leading to the logarithmic scaling of the cumulative regret.
APA
Lumbreras, J. & Tomamiche, M.. (2024). Linear bandits with polylogarithmic minimax regret. Proceedings of Thirty Seventh Conference on Learning Theory, in Proceedings of Machine Learning Research 247:3644-3682 Available from https://proceedings.mlr.press/v247/lumbreras24a.html.

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