2 citations · 3 across the 10 of their papers we have counts for
12 papers
Online LLM Personalization from Pairwise Preferences Across Users
Romain Puech, Georgy Salakhutdinov, Dimitris Bertsimas
Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We study inference-time personal…
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…
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…
Holistic Artificial Intelligence in Medicine; improved performance and explainability
Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou +1
With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to so…
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…
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…
Universal Neurons in GPT2 Language Models
Wes Gurnee, Theo Horsley, Zifan Carl Guo +5
A basic question within the emerging field of mechanistic interpretability is the degree to which neural networks learn the same underlying mechanisms. In other words, are neural m…