3 papers
cs.LG2026
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,…
stat.ML2025
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
Patrick Vossler, Sina Aghaei, Nathan Justin +4
ODTlearn is an open source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the state-of-the-art…
cs.LG2025
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…