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20222026
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cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

Swap Regret Minimization Through Response-Based Approachability

Ioannis Anagnostides, Gabriele Farina, Maxwell Fishelson +2

We consider the problem of minimizing different notions of swap regret in online optimization. These forms of regret are tightly connected to correlated equilibrium concepts in gam…

cs.LG2026

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…

cs.LG2026

On the Computational Complexity of Performative Prediction

Ioannis Anagnostides, Rohan Chauhan, Ioannis Panageas +2

Performative prediction captures the phenomenon where deploying a predictive model shifts the underlying data distribution. While simple retraining dynamics are known to converge l…

cs.LG2025

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…