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20242026
most citedUniversal Neurons in GPT2 Language Models

2 citations · 3 across the 10 of their papers we have counts for

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cs.LG2026

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…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2025★ 1 cited

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…

cs.LG2024

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…

cs.LG2024

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…

cs.LG2024★ 2 cited

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…