Assistant Professor, Technion, Technion
2 papers at NeurIPS 2025
We propose a framework based on evolutionary game theory to model feedback loops in supervised learning, and use it to study how different learning settings affect long-term outcomes.
We extend the study of strategic classification to non-linear classifiers and study how behavior in this regime affects the shape of classifiers and expressivity of model classes.
