3 citations · 4 across the 4 of their papers we have counts for
4 papers
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…
Reconstructing gene expression and knockout effect scores from DNA mutation (Mut2Ex): methodology and application to cancer prediction problems
Maya Ramchandran, Maayan Baron
Building prediction models for outcomes of clinical relevance when only a limited number of mutational features are available causes considerable challenges due to the sparseness a…
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…
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…