Observation-specific explanations through scattered data approximation
<|reference_start|>Observation-specific explanations through scattered data approximation: This work introduces the definition of observation-specific explanations to assign a score to each data point proportional to its importance in the definition of the prediction process. Such explanations involve the identification of the most influential observations for the black-box model of interest. The proposed method involves estimating these explanations by constructing a surrogate model through scattered data approximation utilizing the orthogonal matching pursuit algorithm. The proposed approach is validated on both simulated and real-world datasets.<|reference_end|>
@article{ghidini2024observation-specific,
    title={Observation-specific explanations through scattered data approximation},
    author={Valentina Ghidini, Michael Multerer, Jacopo Quizi, Rohan Sen},
    journal={arXiv preprint arXiv:2404.08747},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08747},
    primaryClass={stat.ML cs.AI cs.LG cs.NA math.NA}
}