AI RESEARCH
The Sample Complexity of Multiple Change Point Identification under Bandit Feedback
arXiv CS.LG
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ArXi:2605.13252v1 Announce Type: cross We study multiple change point localization under bandit feedback. An unknown piecewise-constant function on a compact interval can be queried sequentially at adaptively chosen inputs, and each query returns a noisy evaluation of the function. The goal is to identify a prescribed number of discontinuities, known as change points, within a target precision $\eta$ and confidence level $1-\delta$, while using as few samples as possible.