20 papers
Conformal Risk Minimization for Semi-Supervised Domain Adaptation via Optimal Transport
Manos Giannopoulos, Yi Shen, Michael M. Zavlanos
In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, creating a distribution shift th…
Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Zifan Wang, Riccardo De Santi, Xiaoyu Mo +3
Fine-tuning pre-trained diffusion and flow models to optimize downstream utilities is central to real-world deployment. Existing entropy-regularized methods primarily maximize expe…
An Agentic AI System for Multi-Framework Communication Coding
Bohao Yang, Rui Yang, Joshua M. Biro +14
Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensive, inconsistent, and difficult to sca…
Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach
Zifan Wang, Georgios Pantazis, Sergio Grammatico +2
We study a class of distributionally robust games where agents are allowed to heterogeneously choose their risk aversion with respect to distributional shifts of the uncertainty. I…
Distributionally Robust Federated Learning with Outlier Resilience
Zifan Wang, Xinlei Yi, Xenia Konti +2
Federated learning (FL) enables collaborative model training without direct data sharing, but its performance can degrade significantly in the presence of data distribution perturb…
Federated Flow Matching
Zifan Wang, Anqi Dong, Mahmoud Selim +2
Data today is decentralized, generated and stored across devices and institutions where privacy, ownership, and regulation prevent centralization. This motivates the need to train…