2 papers
cs.LG2025
DeCaFlow: A deconfounding causal generative model
Alejandro Almodóvar, Adrián Javaloy, Juan Parras +2
We introduce DeCaFlow, a deconfounding causal generative model. Training once per dataset using just observational data and the underlying causal graph, DeCaFlow enables accurate c…
cs.LG2025
COPA: Comparing the incomparable in multi-objective model evaluation
Adrián Javaloy, Antonio Vergari, Isabel Valera
In machine learning (ML), we often need to choose one among hundreds of trained ML models at hand, based on various objectives such as accuracy, robustness, fairness or scalability…