23 citations · 70 across the 12 of their papers we have counts for
13 papers
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…
Difficulty-Aware Semantic-ID Optimization for Generative Recommendation
Xin Yu, Stephen Li, Sina Aghaei +6
Semantic-ID-based generative recommendation casts retrieval and ranking as autoregressive generation over hierarchical item identifiers. A common recipe is SFT followed by GRPO, ye…
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…
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription
Patrick Vossler, Nathan Justin, Sina Aghaei +3
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…
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results
Nathanael Jo, Bill Tang, Kathryn Dullerud +3
We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproporti…
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 Classification Trees: Strong Max-Flow Formulations
Sina Aghaei, Andres Gomez, Phebe Vayanos
We consider the problem of learning optimal binary classification trees. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality of heu…
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…
A Predictive Model for Oil Market under Uncertainty: Data-Driven System Dynamics Approach
Sina Aghaei, Amirreza Safari Langroudi, Masoud Fekri
In recent years, there have been a lot of sharp changes in the oil price. These rapid changes cause the traditional models to fail in predicting the price behavior. The main reason…
A Data-Driven Approach for Modeling Stochasticity in Oil Market
Sina Aghaei
Global oil price is an important factor in determining many economic variables in the world's economy. It is generally modeled as a stochastic process and have been studied through…