We investigate the misspecified linear contextual bandit (MLCB) problem, which is a generalization of the linear contextual bandit (LCB) problem. The MLCB ...
We investigate the misspecified linear con- textual bandit (MLCB) problem, which is a generalization of the linear contextual ban- dit (LCB) problem.
A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits. Kei ... Abstract: We investigate the misspecified linear contextual bandit (MLCB) ...
Apr 14, 2021 · We investigate the misspecified linear contextual bandit (MLCB) problem, which is a generalization of the linear contextual bandit (LCB) problem.
We investigate the misspecified linear contextual bandit (MLCB) problem, which is a generalization of the linear contextual bandit (LCB) problem.
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What is the contextual bandit algorithm?
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Jul 16, 2023 · The positioning of this paper is that it introduces a new bandit setting called "Robust contextual linear bandits" which addresses the common ...
Oct 10, 2024 · Motivated by this, we develop a spe- cialized algorithm that achieves optimal bounds for gap-dependent misspecification in linear bandits, thus.
Parameter-Free Multi-Armed Bandit Algorithms with Hybrid Data-Dependent Regret Bounds ... A parameter-free algorithm for misspecified linear contextual bandits.
Note that stochastic linear bandit can be seen as a special case of linear contextual bandits with a fixed decision set Dk = D across all round k ∈ [K]. Similar ...
Oct 29, 2021 · OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits. In AISTATS, volume 108 of Proceedings of Machine ...