Regularizing double machine learning in partially linear endogenous models

Differenced-Based Double Shrinking in Partial Linear Models

Partial linear model is very flexible when the relation between the covariates and responses, either parametric and nonparametric. However, estimation of the regression coefficients is challenging since one must also estimate the nonparametric component simultaneously. As a remedy, the differencing approach, to eliminate the nonparametric component and estimate the regression coefficients, can ...

Persistence Diagrams with Linear Machine Learning Models

Persistence diagrams have been widely recognized as a compact descriptor for characterizing multiscale topological features in data. When many datasets are available, statistical features embedded in those persistence diagrams can be extracted by applying machine learnings. In particular, the ability for explicitly analyzing the inverse in the original data space from those statistical features...

Partially Linear Models

Sparse Partially Linear Additive Models

The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing them to have either a linear or nonlinear effect on the response. However, the assignment of features to the linear and nonlinear groups is typically assumed known. Thus, to make a GPLAM a viable approach in situatio...

Double Deep Machine Learning

Very important breakthroughs in data-centric machine learning algorithms led to impressive performance in ‘transactional’ point applications such as detecting anger in speech, alerts from a Face Recognition system, or EKG interpretation. Nontransactional applications, e.g. medical diagnosis beyond the EKG results, require AI algorithms that integrate deeper and broader knowledge in their proble...