23 citations · 70 across the 12 of their papers we have counts for
7 papers · 1 filter
Learning Risk Scores Robust to Unobserved Confounders
Ryan Edmonds, Yingxiao Ye, Sina Aghaei +3
We consider the problem of learning risk scores to prioritize individuals for scarce resources or interventions, from historical observational data affected by unobserved confoundi…
Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
Minmin Zhang, Sina Aghaei, Soroush Saghafian
Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities, yet their potential for sequential decision-making remains underexplored. In this paper,…
Learning Optimal Classification Trees Robust to Distribution Shifts
Nathan Justin, Sina Aghaei, Andrés Gómez +1
We consider the problem of learning classification trees that are robust to distribution shifts between training and testing/deployment data. This problem arises frequently in high…
Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy
Nathanael Jo, Sina Aghaei, Andrés Gómez +1
The increasing use of machine learning in high-stakes domains -- where people's livelihoods are impacted -- creates an urgent need for interpretable, fair, and highly accurate algo…
Learning Optimal Prescriptive Trees from Observational Data
Nathanael Jo, Sina Aghaei, Andrés Gómez +1
We consider the problem of learning an optimal prescriptive tree (i.e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observati…
Strong Optimal Classification Trees
Sina Aghaei, Andrés Gómez, Phebe Vayanos
Decision trees are among the most popular machine learning models and are used routinely in applications ranging from revenue management and medicine to bioinformatics. In this pap…
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making
Sina Aghaei, Mohammad Javad Azizi, Phebe Vayanos
In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the prod…