论文标题
拆开宣传：通过方差操纵控制图表预测
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation
论文作者
论文摘要
模拟在机器学习中无处不在。特别是在图形学习中，正在部署定向无环图（DAG）的模拟以评估新算法。在文献中，最近有人认为，结构发现（例如notears）的连续优化方法可能正在利用该变量在可用数据中的可分解性，因为它们使用了最小的正方形损失。具体而言，由于结构发现是科学及其他方面的关键问题，因此我们希望对用于测量数据的量表不变（例如，仪表和厘米不应影响算法推断出的因果方向）。在这项工作中，我们通过证明关键结果在多元案例中进一步加强了这一初始的，负面的经验建议，并通过进一步的经验证据来证实。特别是，我们表明我们可以通过目标方差攻击来控制所得图，即使在我们只能部分操纵数据方差的情况下。
Simulations are ubiquitous in machine learning. Especially in graph learning, simulations of Directed Acyclic Graphs (DAG) are being deployed for evaluating new algorithms. In the literature, it was recently argued that continuous-optimization approaches to structure discovery such as NOTEARS might be exploiting the sortability of the variable's variances in the available data due to their use of least square losses. Specifically, since structure discovery is a key problem in science and beyond, we want to be invariant to the scale being used for measuring our data (e.g. meter versus centimeter should not affect the causal direction inferred by the algorithm). In this work, we further strengthen this initial, negative empirical suggestion by both proving key results in the multivariate case and corroborating with further empirical evidence. In particular, we show that we can control the resulting graph with our targeted variance attacks, even in the case where we can only partially manipulate the variances of the data.
