activity
20062022
most citedThe Price of Interpretability

30 citations · 101 across the 13 of their papers we have counts for

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6 papers · 1 filter

stat.ML2019

Personalized Treatment for Coronary Artery Disease Patients: A Machine Learning Approach

Dimitris Bertsimas, Agni Orfanoudaki, Rory B. Weiner

Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that consi…

stat.ML201924 cited

From Predictions to Prescriptions in Multistage Optimization Problems

Dimitris Bertsimas, Christopher McCord

In this paper, we introduce a framework for solving finite-horizon multistage optimization problems under uncertainty in the presence of auxiliary data. We assume the joint distrib…

stat.ML2019

Scalable Holistic Linear Regression

Dimitris Bertsimas, Michael Lingzhi Li

We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinear…

stat.ML2018

Interpretable Clustering via Optimal Trees

Dimitris Bertsimas, Agni Orfanoudaki, Holly Wiberg

State-of-the-art clustering algorithms use heuristics to partition the feature space and provide little insight into the rationale for cluster membership, limiting their interpreta…

stat.ML2018

Imputation of Clinical Covariates in Time Series

Dimitris Bertsimas, Agni Orfanoudaki, Colin Pawlowski

Missing data is a common problem in real-world settings and particularly relevant in healthcare applications where researchers use Electronic Health Records (EHR) and results of ob…

stat.ML2018

Optimization over Continuous and Multi-dimensional Decisions with Observational Data

Dimitris Bertsimas, Christopher McCord

We consider the optimization of an uncertain objective over continuous and multi-dimensional decision spaces in problems in which we are only provided with observational data. We p…