Adaptive Data Augmentation for Thompson Sampling

• URL: http://arxiv.org/abs/2506.14479v1
• Date: Tue, 17 Jun 2025 12:57:33 GMT
• Title: Adaptive Data Augmentation for Thompson Sampling
• Authors: Wonyoung Kim,
• Abstract summary: In linear contextual bandits, the objective is to select actions that maximize cumulative rewards.<n>Thompson Sampling performs well empirically, but it does not achieve optimal regret bounds.<n>This paper proposes a nearly minimax optimal Thompson Sampling for linear contextual bandits.
• Score: 4.441866681085518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In linear contextual bandits, the objective is to select actions that maximize cumulative rewards, modeled as a linear function with unknown parameters. Although Thompson Sampling performs well empirically, it does not achieve optimal regret bounds. This paper proposes a nearly minimax optimal Thompson Sampling for linear contextual bandits by developing a novel estimator with the adaptive augmentation and coupling of the hypothetical samples that are designed for efficient parameter learning. The proposed estimator accurately predicts rewards for all arms without relying on assumptions for the context distribution. Empirical results show robust performance and significant improvement over existing methods.

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