标题： 通过变分推断的生成主成分回归 显示英文标题

标题： Generative Principal Component Regression via Variational Inference

摘要： 操纵复杂系统，如大脑，以修改特定结果的能力具有深远的影响，特别是在精神疾病治疗方面。设计适当操控方法的一种方法是针对预测模型的关键特征。虽然生成潜在变量模型，如概率主成分分析（PPCA），是识别目标的强大工具，但它们在将与低方差结果相关的信息纳入潜在空间方面存在困难。在这种情况下，当刺激目标在潜在空间中设计时，干预效果可能不佳。为了解决这个问题，我们开发了一种基于监督变分自编码器（SVAEs）的新目标，该目标强制将此类信息表示在潜在空间中。该新目标可以与线性模型结合使用，如PPCA，我们称之为生成主成分回归（gPCR）。我们在模拟中展示了与标准PCR和SVAEs相比，gPCR显著提高了操控中的目标选择。作为这些模拟的一部分，我们开发了一种度量标准，用于检测相关信息是否未正确纳入载荷中。然后我们展示了两个与压力和社交行为相关的神经数据集，在这些数据集中，gPCR在预测性能上显著优于PCR，而SVAEs在载荷中表现出较低的相关信息整合。总体而言，这项工作表明，我们的方法在使用潜在变量模型进行目标选择方面，相对于竞争推断方案有显著改进。 显示英文摘要

摘要： The ability to manipulate complex systems, such as the brain, to modify specific outcomes has far-reaching implications, particularly in the treatment of psychiatric disorders. One approach to designing appropriate manipulations is to target key features of predictive models. While generative latent variable models, such as probabilistic principal component analysis (PPCA), is a powerful tool for identifying targets, they struggle incorporating information relevant to low-variance outcomes into the latent space. When stimulation targets are designed on the latent space in such a scenario, the intervention can be suboptimal with minimal efficacy. To address this problem, we develop a novel objective based on supervised variational autoencoders (SVAEs) that enforces such information is represented in the latent space. The novel objective can be used with linear models, such as PPCA, which we refer to as generative principal component regression (gPCR). We show in simulations that gPCR dramatically improves target selection in manipulation as compared to standard PCR and SVAEs. As part of these simulations, we develop a metric for detecting when relevant information is not properly incorporated into the loadings. We then show in two neural datasets related to stress and social behavior in which gPCR dramatically outperforms PCR in predictive performance and that SVAEs exhibit low incorporation of relevant information into the loadings. Overall, this work suggests that our method significantly improves target selection for manipulation using latent variable models over competitor inference schemes.