20 citations · 24 across the 8 of their papers we have counts for
9 papers
ML Compass: Navigating Capability, Cost, and Compliance Trade-offs in AI Model Deployment
Vassilis Digalakis, Ramayya Krishnan, Gonzalo Martin Fernandez +1
We study how organizations should select among competing AI models when user utility, deployment costs, and compliance requirements jointly matter. Widely used capability leaderboa…
Conformalized Decision Risk Assessment
Wenbin Zhou, Agni Orfanoudaki, Shixiang Zhu
In many operational settings, decision-makers must commit to actions before uncertainty resolves, but existing optimization tools rarely quantify how consistently a chosen decision…
Local Causal Discovery for Structural Evidence of Direct Discrimination
Jacqueline Maasch, Kyra Gan, Violet Chen +3
Identifying the causal pathways of unfairness is a critical objective for improving policy design and algorithmic decision-making. Prior work in causal fairness analysis often requ…
Distribution-free risk assessment of regression-based machine learning algorithms
Sukrita Singh, Neeraj Sarna, Yuanyuan Li +3
Machine learning algorithms have grown in sophistication over the years and are increasingly deployed for real-life applications. However, when using machine learning techniques in…
Algorithmic Insurance
Dimitris Bertsimas, Agni Orfanoudaki
When AI systems make errors in high-stakes domains like medical diagnosis or autonomous vehicles, a single algorithmic flaw across varying operational contexts can generate highly…
Optimal Survival Trees
Dimitris Bertsimas, Jack Dunn, Emma Gibson +1
Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models. Survival tree methods adapt these…
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…
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…
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…