Author

Itai Zilberstein

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#machine learning Preprint Oct 2026

Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring

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 this assumption. We focus on outcome-dependent truncation in which an observation appears in the training data depending on its label. Our w...

Itai Zilberstein, I. Anagnostides, Arman Kilic et al. · 0 citations

Jul 2026

Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling

Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically. We address this challenge from two complementary directions. We revisit the connection between DCOPs and pote...

Itai Zilberstein, Pranav Rajbhandari, Steve A. Chien et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Jul 2026

Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values

A novel preference elicitation algorithm for linear utilities that outperforms prior techniques in practice and is applied to heart transplant allocation where a policy must balance competing objectives such as post-transplant outcomes, waitlist mortality, geographic ease, and equity.

Itai Zilberstein, I. Anagnostides, Zachary W. Sollie et al. · 0 citations