论文标题
如果影响功能是答案，那么问题是什么？
If Influence Functions are the Answer, Then What is the Question?
论文作者
论文摘要
影响功能有效地估计删除单个训练数据点对模型学到的参数的影响。尽管影响力估计与线性模型的一对淘汰重新进行良好相吻合，但最近的作品表明，在神经网络中，这种比对通常很差。在这项工作中，我们通过将其分解为五个单独的术语来研究导致这种差异的特定因素。我们研究每个术语对各种架构和数据集的贡献，以及它们如何随着网络宽度和培训时间等因素而变化。尽管实际影响函数估计值可能是非线性网络中保留对一的重新培训的差异，但我们表明它们通常是与其他对象的良好近似值，我们称其为近端Bregman响应函数（PBRF）。由于PBRF仍然可以用来回答许多激励影响功能的问题，例如识别有影响力或标记的示例，因此我们的结果表明，影响功能估计的当前算法比以前的错误分析所暗示的要提供更多信息的结果。
Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters. While influence estimates align well with leave-one-out retraining for linear models, recent works have shown this alignment is often poor in neural networks. In this work, we investigate the specific factors that cause this discrepancy by decomposing it into five separate terms. We study the contributions of each term on a variety of architectures and datasets and how they vary with factors such as network width and training time. While practical influence function estimates may be a poor match to leave-one-out retraining for nonlinear networks, we show they are often a good approximation to a different object we term the proximal Bregman response function (PBRF). Since the PBRF can still be used to answer many of the questions motivating influence functions, such as identifying influential or mislabeled examples, our results suggest that current algorithms for influence function estimation give more informative results than previous error analyses would suggest.
