Jun Wu

According to our database, Jun Wu authored at least 22 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

A Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation.

Jun Wu, Jingrui He

IEEE Trans. Knowl. Data Eng., November, 2023

Trustworthy Transfer Learning: Transferability and Trustworthiness.

Jun Wu, Jingrui He

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Personalized Federated Learning with Parameter Propagation.

Jun Wu, Wenxuan Bao, Elizabeth A. Ainsworth, Jingrui He

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Optimizing the Collaboration Structure in Cross-Silo Federated Learning.

Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He

Proceedings of the International Conference on Machine Learning, 2023

Non-IID Transfer Learning on Graphs.

Jun Wu, Jingrui He, Elizabeth A. Ainsworth

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Dynamic transfer learning with progressive meta-task scheduler.

Jun Wu, Jingrui He

Frontiers Big Data, 2022

Adaptive Transfer Learning for Plant Phenotyping.

Jun Wu, Elizabeth A. Ainsworth, Sheng Wang, Kaiyu Guan, Jingrui He

CoRR, 2022

Distribution-Informed Neural Networks for Domain Adaptation Regression.

Jun Wu, Jingrui He, Sheng Wang, Kaiyu Guan, Elizabeth A. Ainsworth

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Domain Adaptation with Dynamic Open-Set Targets.

Jun Wu, Jingrui He

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

A Unified Meta-Learning Framework for Dynamic Transfer Learning.

Jun Wu, Jingrui He

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning.

Yao Zhou, Jun Wu, Haixun Wang, Jingrui He

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Adaptive Knowledge Transfer on Evolving Domains.

Jun Wu, Hanghang Tong, Elizabeth A. Ainsworth, Jingrui He

Proceedings of the IEEE International Conference on Big Data, 2022

2021

PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network.

Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Körpeoglu, Kannan Achan, Jingrui He

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Indirect Invisible Poisoning Attacks on Domain Adaptation.

Jun Wu, Jingrui He

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

GAN-based Recommendation with Positive-Unlabeled Sampling.

Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Körpeoglu, Kannan Achan, Jingrui He

CoRR, 2020

Robust Decentralized Learning for Neural Networks.

Yao Zhou, Jun Wu, Jingrui He

CoRR, 2020

Continuous Transfer Learning with Label-informed Distribution Alignment.

Jun Wu, Jingrui He

CoRR, 2020

CANON: Complex Analytics of Network of Networks for Modeling Adversarial Activities.

Shane Roach, Connie Ni, Alexei Kopylov, Tsai-Ching Lu, Jiejun Xu, Si Zhang, Boxin Du, Dawei Zhou, Jun Wu, Lihui Liu, Yuchen Yan, Jingrui He, Hanghang Tong

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification.

Jun Wu, Jingrui He, Jiejun Xu

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy.

Jun Wu, Jingrui He

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

2018

ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data.

Jun Wu, Jingrui He, Yongming Liu

CoRR, 2018

ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation.

Jun Wu, Jingrui He, Yongming Liu

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018