33 citations · 117 across the 22 of their papers we have counts for
10 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…
Responsible Machine Learning via Mixed-Integer Optimization
Nathan Justin, Qingshi Sun, Andrés Gómez +1
In the last few decades, Machine Learning (ML) has achieved significant success across domains ranging from healthcare, sustainability, and the social sciences, to criminal justice…
Mixed-feature Logistic Regression Robust to Distribution Shifts
Qingshi Sun, Nathan Justin, Andres Gomez +1
Logistic regression models are widely used in the social and behavioral sciences and in high-stakes domains, due to their simplicity and interpretability properties. At the same ti…
Learning Fair Policies for Multi-stage Selection Problems from Observational Data
Zhuangzhuang Jia, Grani A. Hanasusanto, Phebe Vayanos +1
We consider the problem of learning fair policies for multi-stage selection problems from observational data. This problem arises in several high-stakes domains such as company hir…
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 Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery
Aida Rahmattalabi, Phebe Vayanos, Kathryn Dullerud +1
We study the problem of learning, from observational data, fair and interpretable policies that effectively match heterogeneous individuals to scarce resources of different types.…
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…