5 papers · 1 filter
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…
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…
Multimodal Prescriptive Deep Learning
Dimitris Bertsimas, Lisa Everest, Vasiliki Stoumpou
We introduce a multimodal deep learning framework, Prescriptive Neural Networks (PNNs), that combines ideas from optimization and machine learning, and is, to the best of our knowl…
Deep Trees for (Un)structured Data: Tractability, Performance, and Interpretability
Dimitris Bertsimas, Lisa Everest, Jiayi Gu +2
Decision Trees have remained a popular machine learning method for tabular datasets, mainly due to their interpretability. However, they lack the expressiveness needed to handle hi…
Binary Classification: Is Boosting stronger than Bagging?
Dimitris Bertsimas, Vasiliki Stoumpou
Random Forests have been one of the most popular bagging methods in the past few decades, especially due to their success at handling tabular datasets. They have been extensively s…