Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem
arXiv:2603.10184v2 Announce Type: replace-cross Abstract: Statistical inference with bandit data presents fundamental challenges owing to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theory. Recent work has identified stability~\citep{laiwei82} as a sufficient condition for valid inference under adaptivity. This paper first provides a refined stability condition, stated in terms of the iterates of an online algorithm, and shows that a large class of regularized stochastic-mirror-descent-style algorithms satisfy it. This refined condition allows
The paper represents an incremental academic advancement in statistical theory for adaptive sampling algorithms, building on recent work in stable inference. It reflects ongoing research efforts to improve the theoretical foundations of machine learning, especially for bandit algorithms.
Advanced theoretical understanding of bandit algorithms' stability enables more reliable and robust deployment of AI agents and adaptive systems in critical applications. This contributes to the foundational reliability needed for autonomous AI development.
The refined stability condition and its application to regularized stochastic-mirror-descent style algorithms provide a stronger theoretical basis for ensuring valid inference in adaptive systems. This allows for more predictable and verifiable performance of certain AI algorithms.
- · AI researchers and algorithm developers
- · Sectors using adaptive learning systems (e.g., healthcare, finance)
- · Developers of AI agents
- · Ad-hoc algorithmic approaches lacking theoretical guarantees
Improved theoretical guarantees lead to more robust and trustworthy AI algorithms.
Increased adoption of such algorithms in high-stakes environments due to enhanced reliability.
Acceleration of the development and deployment of complex AI agents that rely on adaptive learning.
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