2 papers
cs.LG2024
No Free Lunch From Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions
Benjamin S. Ruben, William L. Tong, Hamza Tahir Chaudhry +1
Given a fixed budget for total model size, one must choose between training a single large model or combining the predictions of multiple smaller models. We investigate this trade-…
stat.ML2023
Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles
Benjamin S. Ruben, Cengiz Pehlevan
Feature bagging is a well-established ensembling method which aims to reduce prediction variance by combining predictions of many estimators trained on subsets or projections of fe…