2 citations · 3 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
Group Distributionally Robust Machine Learning under Group Level Distributional Uncertainty
Xenia Konti, Yi Shen, Zifan Wang +4
The performance of machine learning (ML) models critically depends on the quality and representativeness of the training data. In applications with multiple heterogeneous data gene…
FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport
Pengxi Liu, Yi Shen, Matthew M. Engelhard +4
Fairness metrics utilizing the area under the receiver operator characteristic curve (AUC) have gained increasing attention in high-stakes domains such as healthcare, finance, and…
Inverse Reinforcement Learning from Non-Stationary Learning Agents
Kavinayan P. Sivakumar, Yi Shen, Zachary Bell +3
In this paper, we study an inverse reinforcement learning problem that involves learning the reward function of a learning agent using trajectory data collected while this agent is…
Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Xenia Konti, Hans Riess, Manos Giannopoulos +4
In this paper, we address the challenge of heterogeneous data distributions in cross-silo federated learning by introducing a novel algorithm, which we term Cross-silo Robust Clust…
Towards Understanding the Impact of Model Size on Differential Private Classification
Yinchen Shen, Zhiguo Wang, Ruoyu Sun +1
Differential privacy (DP) is an essential technique for privacy-preserving. It was found that a large model trained for privacy preserving performs worse than a smaller model (e.g.…