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Found 3 papers, 1 papers with code
Evaluating unsupervised disentangled representation learning for genomic discovery and disease risk prediction
High-dimensional clinical data have become invaluable resources for genetic studies, due to their accessibility in biobank-scale datasets and the development of high performance modeling techniques especially using deep learning.
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression
The key insight of SLOE is that the Sur and Cand\`es (2019) correction can be reparameterized in terms of the \emph{corrupted signal strength}, which is only a function of the estimated parameters $\widehat \beta$.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
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