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Found 14 papers, 10 papers with code
Multi-source domain adaptation for regression
First, we extend a flexible single-source DA algorithm for classification through outcome-coarsening to enable its application to regression problems.
Multi-study R-learner for Heterogeneous Treatment Effect Estimation
To this end, we propose a unifying framework for multi-study heterogeneous treatment effect estimation that is robust to between-study heterogeneity in the nuisance functions and treatment effects.
Defining Replicability of Prediction Rules
In this article I propose an approach for defining replicability for prediction rules.
Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach
Allowing the regression coefficients of tasks to have different sparsity patterns (i. e., different supports), we propose a modeling framework for MTL that encourages models to share information across tasks, for a given covariate, through separately 1) shrinking the coefficient supports together, and/or 2) shrinking the coefficient values together.
Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
In the regression setting, we provide theoretical guidelines based on an analytical transition point to determine whether it is more beneficial to merge or to ensemble for boosting with linear learners.
Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation
Importantly, our approach outperforms multi-study stacking and other standard methods in this application.
Prediction of Hereditary Cancers Using Neural Networks
We validate the models using data from the Cancer Genetics Network.
Cross-Cluster Weighted Forests
Adapting machine learning algorithms to better handle clustering or batch effects within training data sets is important across a wide variety of biological applications.
Extending Models Via Gradient Boosting: An Application to Mendelian Models
Via simulations we show that integration of gradient boosting with an existing Mendelian model can produce an improved model that outperforms both that model and the model built using gradient boosting alone.
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
We study an adversarial loss function for $k$ domains and precisely characterize its limiting behavior as $k$ grows, formalizing and proving the intuition, backed by experiments, that observing data from a larger number of domains helps.
Merging versus Ensembling in Multi-Study Prediction: Theoretical Insight from Random Effects
A critical decision point when training predictors using multiple studies is whether these studies should be combined or treated separately.
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations.
The Fuzzy ROC
The fuzzy ROC extends Receiver Operating Curve (ROC) visualization to the situation where some data points, falling in an indeterminacy region, are not classified.
High-dimensional confounding adjustment using continuous spike and slab priors
In observational studies, estimation of a causal effect of a treatment on an outcome relies on proper adjustment for confounding.
Methodology
