10 papers
Observing the unobserved confounding through its effects: toward randomized trial-like estimates from real-world survival data
Vasiliki Stoumpou, Dimitris Bertsimas, Samuel Singer +1
Background: Randomized controlled trials (RCTs) are costly, time-consuming, and often infeasible, while treatment-effect estimation from observational data is limited by unobserved…
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMM
Dimitris Bertsimas, Nicholas A. G. Johnson
We study the problem of learning a partially observed matrix under the low rank assumption in the presence of fully observed side information that depends linearly on the true unde…
An Interpretable AI Tool for SAVR vs TAVR in Low to Intermediate Risk Patients with Severe Aortic Stenosis
Vasiliki Stoumpou, Maciej Tysarowski, Talhat Azemi +4
Background. Treatment selection for low to intermediate risk patients with severe aortic stenosis between surgical (SAVR) and transcatheter (TAVR) aortic valve replacement remains…
Detection and Localization of Subdural Hematoma Using Deep Learning on Computed Tomography
Vasiliki Stoumpou, Rohan Kumar, Bernard Burman +3
Background. Subdural hematoma (SDH) is a common neurosurgical emergency, with increasing incidence in aging populations. Rapid and accurate identification is essential to guide tim…
Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations
Dimitris Bertsimas, Caio de Prospero Iglesias, Nicholas A. G. Johnson
We study Sparse Multiple Kernel Learning (SMKL), which is the problem of selecting a sparse convex combination of prespecified kernels for support vector binary classification. Unl…
Adaptive Forests For Classification
Dimitris Bertsimas, Yubing Cui
Random Forests (RF) and Extreme Gradient Boosting (XGBoost) are two of the most widely used and highly performing classification and regression models. They aggregate equally weigh…