Arman Kilic — accepted AI-conference papers

2026

Near-Optimal Dynamic Matching via Coarsening with Application to Heart Transplantation

Itai Zilberstein, Ioannis Anagnostides, Zachary Sollie, Arman Kilic, Tuomas Sandholm

ICML 2026poster

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 important step toward addressing this by developing new online matching algorithms based on a coarsening approach. Although …

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2026

Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives

Ioannis Anagnostides, Itai Zilberstein, Zachary Sollie, Arman Kilic, Tuomas Sandholm

ICML 2026spotlight

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-based systems to machine learning and data-driven optimization, we argue that current approaches often overlook a fundamenta…

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