Search Results for author:
Found 8 papers, 4 papers with code
A Brief Tutorial on Sample Size Calculations for Fairness Audits
In fairness audits, a standard objective is to detect whether a given algorithm performs substantially differently between subgroups.
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity.
Is this model reliable for everyone? Testing for strong calibration
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup.
Multi-task Highly Adaptive Lasso
We propose a novel, fully nonparametric approach for the multi-task learning, the Multi-task Highly Adaptive Lasso (MT-HAL).
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
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 predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to predict.
Sequential algorithmic modification with test data reuse
Each modification introduces a risk of deteriorating performance and must be validated on a test dataset.
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees
In the COPD study, BLR and MarBLR dynamically combined the original model with a continually-refitted gradient boosted tree to achieve aAUCs of 0. 924 (95%CI 0. 913-0. 935) and 0. 925 (95%CI 0. 914-0. 935), compared to the static model's aAUC of 0. 904 (95%CI 0. 892-0. 916).
Personalized Online Machine Learning
In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degrees of personalization.
