Avoiding the Price of Adaptivity: Inference in Linear Contextual Bandits via Stability

Research #llm 🔬 Research|Analyzed: Dec 25, 2025 04:31•

Published: Dec 24, 2025 05:00

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1 min read

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Analysis

This ArXiv paper addresses a critical challenge in contextual bandit algorithms: the \

Key Takeaways

•Adaptive sampling in contextual bandits can lead to inflated confidence intervals.
•The Lai-Wei stability condition allows for valid inference without the usual price of adaptivity.
•A penalized EXP4 algorithm is proposed that satisfies the stability condition and achieves minimax optimal regret.

Reference / Citation

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"When stability holds, the ordinary least-squares estimator satisfies a central limit theorem, and classical Wald-type confidence intervals -- designed for i.i.d. data -- become asymptotically valid even under adaptation, \emph{without} incurring the $\\sqrt{d \\log T}$ price of adaptivity."

ArXiv Stats MLDec 24, 2025 05:00

* Cited for critical analysis under Article 32.

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