42 citations · 145 across the 28 of their papers we have counts for
5 papers · 1 filter
Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
Cathy Shyr, Pragya Sur, Giovanni Parmigiani +1
Cross-study replicability is a powerful model evaluation criterion that emphasizes generalizability of predictions. When training cross-study replicable prediction models, it is cr…
Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation
Gabriel Loewinger, Rolando Acosta Nunez, Rahul Mazumder +1
It is increasingly common to encounter prediction tasks in the biomedical sciences for which multiple datasets are available for model training. Common approaches such as pooling d…
Prediction of Hereditary Cancers Using Neural Networks
Zoe Guan, Giovanni Parmigiani, Danielle Braun +1
Family history is a major risk factor for many types of cancer. Mendelian risk prediction models translate family histories into cancer risk predictions based on knowledge of cance…
Cross-Cluster Weighted Forests
Maya Ramchandran, Rajarshi Mukherjee, Giovanni Parmigiani
Building trustworthy machine learning algorithms for biological applications requires adapting to data heterogeneity from different sources, batches, distributions, or studies. We…
Merging versus Ensembling in Multi-Study Prediction: Theoretical Insight from Random Effects
Zoe Guan, Giovanni Parmigiani, Prasad Patil
A critical decision point when training predictors using multiple studies is whether studies should be combined or treated separately. We compare two multi-study prediction approac…