15 citations · 39 across the 11 of their papers we have counts for
12 papers
Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem
Katherine Tieu, Dongqi Fu, Jun Wu +1
In the era of foundation models, Out-of- Distribution (OOD) problems, i.e., the data discrepancy between the training environments and testing environments, hinder AI generalizatio…
Trustworthy Transfer Learning: A Survey
Jun Wu, Jingrui He
Transfer learning aims to transfer knowledge or information from a source domain to a relevant target domain. In this paper, we understand transfer learning from the perspectives o…
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
Wenxuan Bao, Haohan Wang, Jun Wu +1
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized. Through utilizing more training data, FL…
Non-IID Transfer Learning on Graphs
Jun Wu, Jingrui He, Elizabeth Ainsworth
Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorit…
BOBA: Byzantine-Robust Federated Learning with Label Skewness
Wenxuan Bao, Jun Wu, Jingrui He
In federated learning, most existing robust aggregation rules (AGRs) combat Byzantine attacks in the IID setting, where client data is assumed to be independent and identically dis…
A Unified Meta-Learning Framework for Dynamic Transfer Learning
Jun Wu, Jingrui He
Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task. However, most existing works assume both tasks are sampled from a…