50 citations · 59 across the 7 of their papers we have counts for
8 papers
Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling
Iain Carmichael, Andrew H. Song, Richard J. Chen +3
Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology that requires modeling com…
yaglm: a Python package for fitting and tuning generalized linear models that supports structured, adaptive and non-convex penalties
Iain Carmichael, Thomas Keefe, Naomi Giertych +1
The yaglm package aims to make the broader ecosystem of modern generalized linear models accessible to data analysts and researchers. This ecosystem encompasses a range of loss fun…
The folded concave Laplacian spectral penalty learns block diagonal sparsity patterns with the strong oracle property
Iain Carmichael
Structured sparsity is an important part of the modern statistical toolkit. We say a set of model parameters has block diagonal sparsity up to permutations if its elements can be v…
Learning Sparsity and Block Diagonal Structure in Multi-View Mixture Models
Iain Carmichael
Scientific studies increasingly collect multiple modalities of data to investigate a phenomenon from several perspectives. In integrative data analysis it is important to understan…
Joint and individual analysis of breast cancer histologic images and genomic covariates
Iain Carmichael, Benjamin C. Calhoun, Katherine A. Hoadley +8
A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breas…
An exposition of the false confidence theorem
Iain Carmichael, Jonathan P Williams
A recent paper presents the "false confidence theorem" (FCT) which has potentially broad implications for statistical inference using Bayesian posterior uncertainty. This theorem s…