4 papers
Causal Inference for Sequential Settings under Interference and Latent Confounding
Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah
We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian…
Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach
Phevos Paschalidis, Vasiliki Stoumpou, Lisa Everest +11
Transcatheter Aortic Valve Replacement (TAVR) has emerged as a minimally invasive treatment option for patients with severe aortic stenosis, a life-threatening cardiovascular condi…
Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences
Dimitris Bertsimas, Vassilis Digalakis, Yu Ma +1
We consider the problem of retraining machine learning (ML) models when new batches of data become available. Existing approaches greedily optimize for predictive power independent…
Cooperative Multi-Agent Graph Bandits: UCB Algorithm and Regret Analysis
Phevos Paschalidis, Runyu Zhang, Na Li
In this paper, we formulate the multi-agent graph bandit problem as a multi-agent extension of the graph bandit problem introduced by Zhang, Johansson, and Li [CISS 57, 1-6 (2023)]…