5 citations · 7 across the 7 of their papers we have counts for
3 papers · 1 filter
Multi-task Highly Adaptive Lasso
Ivana Malenica, Rachael V. Phillips, Daniel Lazzareschi +3
We propose a novel, fully nonparametric approach for the multi-task learning, the Multi-task Highly Adaptive Lasso (MT-HAL). MT-HAL simultaneously learns features, samples and task…
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
Jean Feng, Alexej Gossmann, Gene Pennello +3
Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm…
Sequential algorithmic modification with test data reuse
Jean Feng, Gene Pennello, Nicholas Petrick +3
After initial release of a machine learning algorithm, the model can be fine-tuned by retraining on subsequently gathered data, adding newly discovered features, or more. Each modi…