Diffusion Models Meet Contextual Bandits

Imad Aouali

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arXiv · 2402.10028

Diffusion Models Meet Contextual Bandits

Imad Aouali

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Abstract

Efficient online decision-making in contextual bandits is challenging, as methods without informative priors often suffer from computational or statistical inefficiencies. In this work, we leverage pre-trained diffusion models as expressive priors to capture complex action dependencies and develop a practical algorithm that efficiently approximates posteriors under such priors, enabling both fast updates and sampling. Empirical results demonstrate the effectiveness and versatility of our approach across diverse contextual bandit settings.

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Imad Aouali. 2024-02-15. Diffusion Models Meet Contextual Bandits. https://arxiv.org/abs/2402.10028

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