33 citations · 117 across the 22 of their papers we have counts for
5 papers · 1 filter
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 and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment
Bill Tang, Çağıl Koçyiğit, 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…
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…
Deploying a Robust Active Preference Elicitation Algorithm on MTurk: Experiment Design, Interface, and Evaluation for COVID-19 Patient Prioritization
Caroline M. Johnston, Patrick Vossler, Simon Blessenohl +1
Preference elicitation leverages AI or optimization to learn stakeholder preferences in settings ranging from marketing to public policy. The online robust preference elicitation p…