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.

本研究介绍了观测特定解释的定义，通过为每个数据点分配与其在预测过程中的重要性成比例的分数。这种解释涉及对感兴趣的黑匣子模型中最具影响力的观测的识别。通过利用正交匹配追踪算法构建散乱数据逼近的代理模型来估计这些解释的建议方法。该方法在模拟和真实世界数据集上得到验证。

基于散乱数据逼近的观测特定解释

Observation-specific explanations through scattered data approximation

The great success of modern machine learning models on large datasets is
contingent on extensive computational resources with high financial and
environmental costs. One way to address this is by extracting subsets that
generalize on par with the full data. In this work, we propose a general
framework, GRAD-MATCH, which finds subsets that closely match the gradient of
the training or validation set. We find such subsets effectively using an
orthogonal matching pursuit algorithm. We show rigorous theoretical and
convergence guarantees of the proposed algorithm and, through our extensive
experiments on real-world datasets, show the effectiveness of our proposed
framework. We show that GRAD-MATCH significantly and consistently outperforms
several recent data-selection algorithms and achieves the best
accuracy-efficiency trade-off. GRAD-MATCH is available as a part of the CORDS
toolkit: https://github.com/decile-team/cords.

本文提出了 GRAD-MATCH 作为一种有效的数据子集提取算法，该算法使用正交匹配追踪算法有效地找到与训练或验证集的梯度相匹配的数据子集，实验表明该算法在精度和效率上都有显著提升。