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cs.LG2024
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…
cs.LG2024★ 1 cited
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…
cs.LG2024★ 1 cited
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…