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Stochastic Graph Bandit Learning with Side-Observations
In this paper, we investigate the stochastic contextual bandit with general function space and graph feedback.
Provably Efficient Learning in Partially Observable Contextual Bandit
We then show how causal bounds can be applied to improving classical bandit algorithms and affect the regrets with respect to the size of action sets and function spaces.
Dual Instrumental Method for Confounded Kernelized Bandits
The contextual bandit problem is a theoretically justified framework with wide applications in various fields.
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