6 papers
Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring
Itai Zilberstein, Ioannis Anagnostides, Arman Kilic +1
Modern machine learning models are typically trained on data assumed to be representative of the predictive tasks the model will face in the future, but practical datasets violate…
Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values
Itai Zilberstein, Ioannis Anagnostides, Zachary W Sollie +2
Preference elicitation is essential for aligning AI systems with human values. Prior approaches (e.g., for organ allocation) often ask stakeholders to compare the decisions of an a…
Learning Potentials for Dynamic Matching and Application to Heart Transplantation
Itai Zilberstein, Ioannis Anagnostides, Zachary W. Sollie +2
Each year, thousands of patients in need of heart transplants face life-threatening wait times due to organ scarcity. While allocation policies aim to maximize population-level out…
Near-Optimal Dynamic Matching via Coarsening with Application to Heart Transplantation
Itai Zilberstein, Ioannis Anagnostides, Zachary W. Sollie +2
Online matching has been a mainstay in domains such as Internet advertising and organ allocation, but practical algorithms often lack strong theoretical guarantees. We take an impo…
Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives
Ioannis Anagnostides, Itai Zilberstein, Zachary W. Sollie +2
The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly transitioning from rigid, rule-base…
Policy Optimization for Dynamic Heart Transplant Allocation
Ioannis Anagnostides, Zachary W. Sollie, Arman Kilic +1
Heart transplantation is a viable path for patients suffering from advanced heart failure, but this lifesaving option is severely limited due to donor shortage. Although the curren…