The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

Altered microglial states affect neuroinflammation, neurodegeneration, and disease but
remain poorly understood. Here, we report 194,000 single-nucleus microglial transcriptomes …

Cerebrovascular dysregulation is a hallmark of Alzheimer's disease (AD), but the changes
that occur in specific cell types have not been fully characterized. Here, we profile single …

Recent work has identified dozens of non-coding loci for Alzheimer's disease (AD) risk, but
their mechanisms and AD transcriptional regulatory circuitry are poorly understood. Here …

Safe anytime-valid inference (SAVI) provides measures of statistical evidence and certainty—
e-processes for testing and confidence sequences for estimation—that remain valid at all …

We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …

An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …

Students in statistics or data science usually learn early on that when the sample size n is
large relative to the number of variables p, fitting a logistic model by the method of maximum …

New technologies have led to vast troves of large and complex data sets across many
scientific domains and industries. People routinely use machine learning techniques not …