88 citations · 116 across the 14 of their papers we have counts for
3 papers · 1 filter
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…