algorithm 3
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Noise-Adaptive Thompson Sampling for Linear Contextual Bandits
Linear contextual bandits represent a fundamental class of models with numerous real-world applications, and it is critical to developing algorithms that can effectively manage noise with unknown variance, ensuring provable guarantees for both worst-case constant-variance noise and deterministic reward scenarios.
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