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math.ST2026
Pooling Versus Ensembling for Ridge Regression Under Covariate Shift
Maya Ramchandran, Rajarshi Mukherjee
Datasets in many settings naturally partition into clusters arising from sub-populations, batch effects, or aggregation across multiple sources. A common response to such heterogen…
math.ST2021★ 3 cited
On Ensembling vs Merging: Least Squares and Random Forests under Covariate Shift
Maya Ramchandran, Rajarshi Mukherjee
It has been postulated and observed in practice that for prediction problems in which covariate data can be naturally partitioned into clusters, ensembling algorithms based on suit…