8 citations · 33 across the 8 of their papers we have counts for
8 papers
AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews
Keith Tyser, Ben Segev, Gaston Longhitano +9
Automatic reviewing helps handle a large volume of papers, provides early feedback and quality control, reduces bias, and allows the analysis of trends. We evaluate the alignment o…
Interpretable Prediction and Feature Selection for Survival Analysis
Mike Van Ness, Madeleine Udell
Survival analysis is widely used as a technique to model time-to-event data when some data is censored, particularly in healthcare for predicting future patient risk. In such setti…
OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models
Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than opt…
OptiMUS: Optimization Modeling Using MIP Solvers and large language models
Ali AhmadiTeshnizi, Wenzhi Gao, Madeleine Udell
Optimization problems are pervasive across various sectors, from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand ra…
Interpretable Survival Analysis for Heart Failure Risk Prediction
Mike Van Ness, Tomas Bosschieter, Natasha Din +3
Survival analysis, or time-to-event analysis, is an important and widespread problem in healthcare research. Medical research has traditionally relied on Cox models for survival an…
ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles
Brian Liu, Miaolan Xie, Haoyue Yang +1
ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning. The algorithms in this packa…