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New algorithm precisely locates change points with bandit feedback

Researchers have developed a new adaptive algorithm for identifying multiple change points in data under bandit feedback. This algorithm aims to precisely locate discontinuities in a piecewise-constant function with minimal samples. The study establishes theoretical bounds on the algorithm's sample complexity, revealing that it depends not only on the magnitude of the jumps but also on the relative positions of these change points. AI

影响 Provides a theoretical framework for analyzing data with discontinuities, potentially improving models that rely on sequential data analysis.

排序理由 The cluster contains an academic paper detailing a new algorithm and theoretical analysis.

在 arXiv stat.ML 阅读 →

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New algorithm precisely locates change points with bandit feedback

报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Maximilian Graf, Victor Thuot ·

    The Sample Complexity of Multiple Change Point Identification under Bandit Feedback

    arXiv:2605.13252v1 Announce Type: new Abstract: 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 th…

  2. arXiv stat.ML TIER_1 English(EN) · Victor Thuot ·

    The Sample Complexity of Multiple Change Point Identification under Bandit Feedback

    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…