The development of early non-invasive diagnosis methods and identification of novel
biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective …

Much work has been done recently to make neural networks more interpretable, and one
approach is to arrange for the network to use only a subset of the available features. In linear …

Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series.
However, as the number of component series is increased, the VAR model becomes …

Much work has been done recently to make neural networks more interpretable, and one
approach is to arrange for the network to use only a subset of the available features. In linear …

Abstract Generalized Additive Models (GAMs) are a family of flexible and interpretable
models with old roots in statistics. GAMs are often used with pairwise interactions to improve …

Alzheimer's disease (AD) is a neurodegenerative brain disease, and it is challenging to
mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain …

Despite encouraging results from recent developments in transfer learning for adapting pre-
trained model to downstream tasks the performance of model probing is still lagging behind …

We propose a generalization of the lasso that allows the model coefficients to vary as a
function of a general set of some prespecified modifying variables. These modifiers might be …

Estimation of heterogeneous treatment effects is an active area of research in causal
inference. Most of the existing methods, however, focus on estimating the conditional …

In this paper, we consider fitting a flexible and interpretable additive regression model in a
data‐rich setting. We wish to avoid pre‐specifying the functional form of the conditional …