367 citations · 456 across the 9 of their papers we have counts for
6 papers · 1 filter
Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach
Dimitris Bertsimas, Leonard Boussioux
Accurate time series forecasting is critical for a wide range of problems with temporal data. Ensemble modeling is a well-established technique for leveraging multiple predictive m…
Reducing Air Pollution through Machine Learning
Dimitris Bertsimas, Leonard Boussioux, Cynthia Zeng
This paper presents a data-driven approach to mitigate the effects of air pollution from industrial plants on nearby cities by linking operational decisions with weather conditions…
TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling
Kimberly Villalobos Carballo, Liangyuan Na, Yu Ma +4
Tabular medical records remain the most readily available data format for applying machine learning in healthcare. However, traditional data preprocessing ignores valuable contextu…
Integrated multimodal artificial intelligence framework for healthcare applications
Luis R. Soenksen, Yu Ma, Cynthia Zeng +7
Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities…
Holistic Deep Learning
Dimitris Bertsimas, Kimberly Villalobos Carballo, Léonard Boussioux +3
This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performa…
Hurricane Forecasting: A Novel Multimodal Machine Learning Framework
Léonard Boussioux, Cynthia Zeng, Théo Guénais +1
This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sourc…