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20152023
most citedRepresentation via Representations: Domain Generalization via Adversarially Learned Invariant Representations

11 citations · 21 across the 6 of their papers we have counts for

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2 papers · 1 filter

stat.ML2022★ 1 cited

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…

stat.ML2019★ 2 cited

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…