1 citations · 1 across the 13 of their papers we have counts for
10 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…
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…
Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling
Itai Zilberstein, Pranav Rajbhandari, Steve Chien +1
Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too…
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…
MILP the Gap: An Automated Reasoning Approach to Condorcet's Paradox
Itai Zilberstein, Ratip Emin Berker, George Li +1
Computational social choice studies how conflicting individual preferences are aggregated into a collective decision, with applications to elections, AI evaluation/alignment, and r…
Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning
Akseli Kangaslahti, Itai Zilberstein, Alberto Candela +1
The Dynamic Targeting (DT) mission concept is an approach to satellite observation in which a lookahead sensor gathers information about the upcoming environment and uses this info…
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…
Scalable Dynamic Distributed Constraint Optimization with Metareasoning and Application to Continual Satellite Operations
Itai Zilberstein, Steve Chien
Dynamic distributed constraint optimization problems (DDCOPs) provide a general framework for coordinating autonomous agents in changing environments. However, existing DDCOP formu…