5 citations · 7 across the 7 of their papers we have counts for
7 papers
Realistic CDSS Drug Dosing with End-to-end Recurrent Q-learning for Dual Vasopressor Control
Will Y. Zou, Jean Feng, Alexandre Kalimouttou +3
Reinforcement learning (RL) applications in Clinical Decision Support Systems (CDSS) frequently encounter skepticism because models may recommend inoperable dosing decisions. We pr…
A Brief Tutorial on Sample Size Calculations for Fairness Audits
Harvineet Singh, Fan Xia, Mi-Ok Kim +3
In fairness audits, a standard objective is to detect whether a given algorithm performs substantially differently between subgroups. Properly powering the statistical analysis of…
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
Jean Feng, Adarsh Subbaswamy, Alexej Gossmann +7
After a machine learning (ML)-based system is deployed, monitoring its performance is important to ensure the safety and effectiveness of the algorithm over time. When an ML algori…
Is this model reliable for everyone? Testing for strong calibration
Jean Feng, Alexej Gossmann, Romain Pirracchio +3
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous…
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…