8 papers
Distributionally Robust Optimization with Decision-Dependent Information Discovery
Qing Jin, Angelos Georghiou, Phebe Vayanos +1
We study two-stage distributionally robust optimization (DRO) problems with decision-dependent information discovery (DDID) wherein (a portion of) the uncertain parameters are reve…
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…
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…
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 and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment
Bill Tang, ÃaÄıl KoçyiÄit, Eric Rice +1
We study the problem of allocating scarce societal resources of different types (e.g., permanent housing, deceased donor kidneys for transplantation, ventilators) to heterogeneous…
Toward AI Matching Policies in Homeless Services: A Qualitative Study with Policymakers
Caroline M. Johnston, Olga Koumoundouros, Angel Hsing-Chi Hwang +3
Artificial intelligence researchers have proposed various data-driven algorithms to improve the processes that match individuals experiencing homelessness to scarce housing resourc…