Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making. - Sina Aghaei; Mohammad Javad Azizi; Phebe Vayanos (AAAI, 2019)

Learning Optimal Classification Trees: Strong Max-Flow Formulations. - Sina Aghaei; Andrés Gómez 001; Phebe Vayanos (CoRR, 2020)

Learning Optimal Prescriptive Trees from Observational Data. - Nathanael Jo; Sina Aghaei; Andrés Gómez 001; Phebe Vayanos (CoRR, 2021)

Learning Optimal Fair Classification Trees. - Nathanael Jo; Sina Aghaei; Jack Benson; Andrés Gómez 001; Phebe Vayanos (CoRR, 2022)

Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results. - Nathanael Jo; Bill Tang; Kathryn Dullerud; Sina Aghaei; Eric Rice; Phebe Vayanos (AAAI, 2023)

Learning Optimal Fair Decision Trees: Trade-offs Between Interpretability, Fairness, and Accuracy. - Nathanael Jo; Sina Aghaei; Jack Benson; Andrés Gómez 001; Phebe Vayanos (AIES, 2023)

ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription. - Patrick Vossler; Sina Aghaei; Nathan Justin; Nathanael Jo; Andrés Gómez 001; Phebe Vayanos (CoRR, 2023)

Learning Optimal Classification Trees Robust to Distribution Shifts. - Nathan Justin; Sina Aghaei; Andrés Gómez 001; Phebe Vayanos (CoRR, 2023)

Strong Optimal Classification Trees. - Sina Aghaei; Andrés Gómez 001; Phebe Vayanos (Oper. Res., 2025)