3 papers
cs.LG2026
Toward Enhancing Representation Learning in Federated Multi-Task Settings
Mehdi Setayesh, Mahdi Beitollahi, Yasser H. Khalil +1
Federated multi-task learning (FMTL) seeks to collaboratively train customized models for users with different tasks while preserving data privacy. Most existing approaches assume…
cs.LG2025
CoUn: Empowering Machine Unlearning via Contrastive Learning
Yasser H. Khalil, Mehdi Setayesh, Hongliang Li
Machine unlearning (MU) aims to remove the influence of specific "forget" data from a trained model while preserving its knowledge of the remaining "retain" data. Existing MU metho…
cs.CV2022
Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation
Yifan Wang, Lin Zhang, Ran Song +3
Universal domain adaptation (UniDA) aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge on the label set, which…