In this paper, we study the problem of feature selection in distributed stochastic multi-arm bandits, in which M agents work collaboratively to choose optimal ...
Jun 2, 2023 · Our model is closely related to the distributed bandits considered in [9], where the agents face the same bandit model, and the agents ...
Jun 9, 2021 · We study two model selection settings in stochastic linear bandits (LB). In the first setting, which we refer to as feature selection, the expected reward of ...
Missing: Distributed | Show results with:Distributed
in bandit-over-bandits algorithms is that no information is shared among the models (bandit algorithms), i.e., when a bandit algorithm is used to take an action ...
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Stochastic Linear Optimization under Bandit Feedback · Linearly Parameterized Bandits · Regret Bound Balancing and Elimination for Model Selection in Bandits and ...
Jun 11, 2021 · SIAM journal on computing, 32(1):48–77. ... and nonstochastic multiarmed bandit problems. Foundations and Trends in Machine Learning. ... bandits.
Stochastic linear optimization under bandit feedback. Deshpande, Y., and Montanari, A. 2012. Linear bandits in high dimension and recommendation systems. In ...
Model selection in the context of bandit optimization is a challenging problem, as it requires balancing exploration and exploitation not only for action ...
Oct 22, 2024 · Linear Bandits. In stochastic linear bandit (LB) problems, actions are represented by feature vectors, and the expected reward for each ...
We investigate meta-learning procedures in the setting of stochastic linear bandits tasks. The goal is to select a learning algorithm which works well on...