1 citations · 3 across the 9 of their papers we have counts for
11 papers
RocketPFN: Accurate Time Series Classification via In-Context Learning
Franco Martino O'Rourke, Ana Trisovic, Dimitris Bertsimas
We introduce RocketPFN, a training-free pipeline for time series classification that combines random convolutional feature extraction (Rocket) with in-context classification via a…
A Causal DAG Prior for Synthetic Time-Series Classification Datasets
Franco Martino O'Rourke, Ana Trisovic, Dimitris Bertsimas
A Prior-data fitted Network learns the posterior predictive induced by its training prior; bringing this paradigm to multivariate time-series classification therefore calls for a s…
A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms
Catherine Ning, Yu Ma, Cindy Beini Wang +4
Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We developed a multimodal machi…
Robustifying and Selecting Cohort-Appropriate Prognostic Models under Distributional Shifts
Dimitris Bertsimas, Carol Gao, Angelos G. Koulouras +1
External validation is widely regarded as the gold standard for prognostic model evaluation. In this study, we challenge the assumption that successful external calibration guarant…
Early Warning Index for Patient Deteriorations in Hospitals
Dimitris Bertsimas, Yu Ma, Kimberly Villalobos Carballo +5
Hospitals lack automated systems to harness the growing volume of heterogeneous clinical and operational data to effectively forecast critical events. Early identification of patie…
Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach
Phevos Paschalidis, Vasiliki Stoumpou, Lisa Everest +11
Transcatheter Aortic Valve Replacement (TAVR) has emerged as a prominent, minimally invasive treatment for patients with severe aortic stenosis, a life-threatening cardiovascular c…
From Data to Uncertainty Sets: a Machine Learning Approach
Dimitris Bertsimas, Benjamin Boucher
Existing approaches of prescriptive analytics -- where inputs of an optimization model can be predicted by leveraging covariates in a machine learning model -- often attempt to opt…
Optimal Control of Fluid Restless Multi-armed Bandits: A Machine Learning Approach
Dimitris Bertsimas, Cheol Woo Kim, José Niño-Mora
We present a novel machine learning framework for the optimal control of fluid restless multi-armed bandit problems (FRMABPs) with state equations that are either affine or quadrat…
Policy Trees for Prediction: Interpretable and Adaptive Model Selection for Machine Learning
Dimitris Bertsimas, Matthew Peroni
As a multitude of capable machine learning (ML) models become widely available in forms such as open-source software and public APIs, central questions remain regarding their use i…
M3H: Multimodal Multitask Machine Learning for Healthcare
Dimitris Bertsimas, Yu Ma
Developing an integrated many-to-many framework leveraging multimodal data for multiple tasks is crucial to unifying healthcare applications ranging from diagnoses to operations. I…
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
Dimitris Bertsimas, Vassilis Digalakis, Yu Ma +1
We consider the problem of retraining machine learning (ML) models when new batches of data become available. Existing approaches greedily optimize for predictive power independent…