Score-based change point detection via tracking the best of infinitely many experts
<|reference_start|>Score-based change point detection via tracking the best of infinitely many experts: We suggest a novel algorithm for online change point detection based on sequential score function estimation and tracking the best expert approach. The core of the procedure is a version of the fixed share forecaster for the case of infinite number of experts and quadratic loss functions. The algorithm shows a promising performance in numerical experiments on artificial and real-world data sets. We also derive new upper bounds on the dynamic regret of the fixed share forecaster with varying parameter, which are of independent interest.<|reference_end|>
@article{markovich2024score-based,
    title={Score-based change point detection via tracking the best of infinitely
  many experts},
    author={Anna Markovich, Nikita Puchkin},
    journal={arXiv preprint arXiv:2408.14073},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14073},
    primaryClass={cs.LG stat.ME stat.ML}
}