论文标题
快速学习速度更快
Fast Instrument Learning with Faster Rates
论文作者
论文摘要
我们研究了高维仪器的非线性仪器变量（IV）回归。我们提出了一种简单的算法，该算法结合了内核化的IV方法和一种任意的自适应回归算法，该算法可作为黑匣子访问。我们的算法享受更快的融合，并适应了信息潜在功能的维度，同时避免了昂贵的最小优化程序，这对于建立相似的保证是必要的。它进一步带来了灵活的机器学习模型的好处，可以将基于可能性的不确定性量化，基于可能性的模型选择和模型平均。仿真研究证明了我们方法的竞争性能。
We investigate nonlinear instrumental variable (IV) regression given high-dimensional instruments. We propose a simple algorithm which combines kernelized IV methods and an arbitrary, adaptive regression algorithm, accessed as a black box. Our algorithm enjoys faster-rate convergence and adapts to the dimensionality of informative latent features, while avoiding an expensive minimax optimization procedure, which has been necessary to establish similar guarantees. It further brings the benefit of flexible machine learning models to quasi-Bayesian uncertainty quantification, likelihood-based model selection, and model averaging. Simulation studies demonstrate the competitive performance of our method.
