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
There has been a surge of recent work on correlated equilibrium concepts in Markov games. However, existing results focus on concepts weaker than normal-form correlated equilibria (NFCEs), leaving open the more challenging question of computing such equilibria, which goes back to the seminal work of Papadimitriou and R...
I. Anagnostides, Constantinos Daskalakis, Gabriele Farina et al.· 1 citation
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
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
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