88 citations · 116 across the 14 of their papers we have counts for
14 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…
The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?
Vassilis Digalakis, Christophe Pérignon, Sébastien Saurin +1
Organizations often have an incumbent predictive model in production when new data sources become available. Because historical training data lack the new features, a challenger mo…
Interpretable Time Series Autoregression for Periodicity Quantification
Xinyu Chen, Vassilis Digalakis, Lijun Ding +2
Time series autoregression (AR) is a classical tool for modeling auto-correlations and periodic structures in real-world systems. We revisit this model from an interpretable machin…
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…
BackboneLearn: A Library for Scaling Mixed-Integer Optimization-Based Machine Learning
Vassilis Digalakis, Christos Ziakas
We present BackboneLearn: an open-source software package and framework for scaling mixed-integer optimization (MIO) problems with indicator variables to high-dimensional problems.…
Data Analytics with Differential Privacy
Vassilis Digalakis
Differential privacy is the state-of-the-art definition for privacy, guaranteeing that any analysis performed on a sensitive dataset leaks no information about the individuals whos…
Improving Stability in Decision Tree Models
Dimitris Bertsimas, Vassilis Digalakis
Owing to their inherently interpretable structure, decision trees are commonly used in applications where interpretability is essential. Recent work has focused on improving variou…
Decarbonizing OCP
Dimitris Bertsimas, Ryan Cory-Wright, Vassilis Digalakis
We present our collaboration with the OCP Group, one of the world's largest producers of phosphate and phosphate-based products, to reduce OCP's carbon emissions significantly. We…
Improved Pan-Private Stream Density Estimation
Vassilis Digalakis, George N. Karystinos, Minos N. Garofalakis
Differential privacy is a rigorous definition for privacy that guarantees that any analysis performed on a sensitive dataset leaks no information about the individuals whose data a…
Slowly Varying Regression under Sparsity
Dimitris Bertsimas, Vassilis Digalakis, Michael Linghzi Li +1
We present the framework of slowly varying regression under sparsity, allowing sparse regression models to exhibit slow and sparse variations. The problem of parameter estimation i…
Where to locate COVID-19 mass vaccination facilities?
Dimitris Bertsimas, Vassilis Digalakis, Alexander Jacquillat +2
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to…
Frequency Estimation in Data Streams: Learning the Optimal Hashing Scheme
Dimitris Bertsimas, Vassilis Digalakis
We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. Contrary to state-of-the-art streaming frequ…
From predictions to prescriptions: A data-driven response to COVID-19
Dimitris Bertsimas, Léonard Boussioux, Ryan Cory Wright +18
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a d…
The Backbone Method for Ultra-High Dimensional Sparse Machine Learning
Dimitris Bertsimas, Vassilis Digalakis
We present the backbone method, a generic framework that enables sparse and interpretable supervised machine learning methods to scale to ultra-high dimensional problems. We solve…