Kernel Regularized Least Squares (KRLS) is a popular method for flexibly estimating models that may have complex relationships between variables. However, its usefulness to many researchers is limited for two reasons. First, existing approaches are inflexible and do not allow KRLS to be combined with theoretically-motivated extensions such as fixed effects or non-linear outcomes. Second, estimation is extremely computationally intensive for even modestly sized datasets. Our paper addresses both concerns by introducing generalized KRLS (gKRLS). We note that KRLS can be re-formulated as a hierarchical model thereby allowing easy inference and modular model construction. Computationally, we also implement random sketching to dramatically accelerate estimation while incurring a limited penalty in estimation quality. We demonstrate that gKRLS can be fit on datasets with tens of thousands of observations in under one minute. Further, state-of-the-art techniques that require fitting the model over a dozen times (e.g. meta-learners) can be estimated quickly.

翻译：内核正规化最小方(KRLS)是灵活估算模型的一种常用方法,这些模型在变量之间可能有复杂的关系。然而,它对许多研究人员的用处有限,原因有二。 首先,现有方法不灵活,不能使KRLS与固定效应或非线性结果等具有理论动机的扩展相结合。 其次,对即使规模不大的数据集,估算在计算上也是极其密集的。我们的文件通过采用通用的KRLS(gKRLS)来处理这两个关切问题。我们注意到,KRLS可以重新形成一个等级模型,从而便于推论和模块模型的构建。据计算,我们还随机绘制草图,以大大加快估算速度,同时在估计质量方面受到有限的处罚。我们证明,GKRLS可以在一分钟内与数万次观测的数据集相匹配。此外,需要使模型适应十次以上的最先进的技术(例如元激光器)可以快速估算。