arXiv:2502.08870v2 Announce Type: replace Abstract: We provide an approach for the analysis of randomised exploration algorithms like Thompson sampling that does not rely on forced optimism or posterior inflation. With this, we demonstrate that in the $d$-dimensional linear bandit setting, when the action space is smooth and strongly convex, randomised exploration algorithms enjoy an $n$-step regret bound of the order $O(d\sqrt{n} \log(n))$. Notably, this shows for the first time that there exist non-trivial linear bandit settings where Thompson sampling can achieve optimal dimension dependenc
This paper offers a novel analytical approach to understanding randomized exploration in linear bandits, published in 2026, building on foundational work in AI/ML algorithms.
It demonstrates a path towards optimal performance in certain 'smooth' and 'strongly convex' linear bandit settings, which significantly improves the theoretical understanding and potential practical efficiency of algorithms like Thompson sampling.
The theoretical understanding of randomized exploration algorithms in specific machine learning contexts is now more robust, potentially enabling more efficient and reliable AI agent development.
- · AI/ML researchers
- · Developers of AI agents
- · Industries using reinforcement learning for decision-making
Improved theoretical guarantees for Thompson sampling and similar algorithms lead to more predictable and robust AI system design.
This enhanced understanding could accelerate the development and deployment of autonomous AI agents in areas like resource allocation and personalized recommendations.
More efficient and theoretically grounded AI agents might contribute to the broader 'AI agents' narrative by making complex automation more feasible and reliable.
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Read at arXiv cs.LG