影响诊断的统计与计算保证
Statistical and Computational Guarantees for Influence Diagnostics
AI总结：
本文为影响函数和近似最大影响扰动建立了有限样本统计界限与计算复杂度界限，并通过广义线性模型和大规模注意力模型在合成及真实数据上进行了验证。
摘要：
影响函数和近似最大影响扰动等影响诊断方法在机器学习及人工智能领域应用中十分流行。影响诊断是识别有影响的数据点或数据点子集的有力统计工具。我们利用高效的逆海森向量积实现，为影响函数和近似最大影响扰动建立了有限样本统计界限以及计算复杂度界限。我们通过在合成数据和真实数据上的广义线性模型和基于注意力的大规模模型来展示我们的结果。
英文摘要：
Influence diagnostics such as influence functions and approximate maximum influence perturbations are popular in machine learning and in AI domain applications. Influence diagnostics are powerful statistical tools to identify influential datapoints or subsets of datapoints. We establish finite-sample statistical bounds, as well as computational complexity bounds, for influence functions and approximate maximum influence perturbations using efficient inverse-Hessian-vector product implementations. We illustrate our results with generalized linear models and large attention based models on synthetic and real data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
