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    This study introduces Extreme-Region Upper Confidence Bound (ER-UCB), a novel strategy for efficient automated machine learning (AutoML). ER-UCB optimizes algorithm selection and hyper-parameter tuning, outperforming existing methods in various settings.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Optimization

    Background:

    • Automated Machine Learning (AutoML) seeks to automate the configuration of learning systems.
    • Core AutoML subtasks include algorithm selection and hyper-parameter tuning.
    • Existing methods struggle with inefficient search in large, redundant joint hyper-parameter spaces.

    Purpose of the Study:

    • To develop a more efficient search strategy for AutoML.
    • To improve the process of algorithm selection and hyper-parameter tuning.
    • To introduce a novel multi-armed bandit approach for optimizing AutoML configurations.

    Main Methods:

    • A cascaded approach for algorithm selection and hyper-parameter tuning.
    • Formulating algorithm selection as a multi-armed bandit problem.
    • Proposing the Extreme-Region Upper Confidence Bound (ER-UCB) strategy to maximize the extreme-region of feedback distribution.

    Main Results:

    • Developed ER-UCB-S for stationary distributions with an O(Klnn) regret upper bound.
    • Developed ER-UCB-N for non-stationary settings with an O(Knν) regret upper bound.
    • Empirical studies demonstrated the effectiveness and outperformance of ER-UCB-S/N.

    Conclusions:

    • ER-UCB provides a more efficient and effective approach to AutoML.
    • The proposed algorithms offer improved regret bounds in both stationary and non-stationary settings.
    • ER-UCB enhances the automated configuration of machine learning systems.