1 citations · 1 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Aligning Data-Driven Predictors with Allocation: A Decision-Focused Approach to Survival Analysis
Itai Zilberstein, Ioannis Anagnostides, Tuomas Sandholm
Machine learning predictors have become essential tools for guiding automated decision making. However, a major misalignment persists: predictive models are typically optimized in…
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…