Kaili Ma

According to our database, Kaili Ma authored at least 16 papers between 2020 and 2024.

Collaborative distances:

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

2024

Solving the non-submodular network collapse problems via Decision Transformer.

Kaili Ma, Han Yang, Shanchao Yang, Kangfei Zhao, Lanqing Li, Yongqiang Chen, Junzhou Huang, James Cheng, Yu Rong

Neural Networks, 2024

2023

GRACE: A General Graph Convolution Framework for Attributed Graph Clustering.

Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han

ACM Trans. Knowl. Discov. Data, April, 2023

Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring.

Shanchao Yang, Kaili Ma, Baoxiang Wang, Tianshu Yu, Hongyuan Zha

Trans. Mach. Learn. Res., 2023

Calibrating and Improving Graph Contrastive Learning.

Kaili Ma, Garry Yang, Han Yang, Yongqiang Chen, James Cheng

Trans. Mach. Learn. Res., 2023

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization.

Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Pareto Invariant Risk Minimization.

Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng

CoRR, 2022

Invariance Principle Meets Out-of-Distribution Generalization on Graphs.

Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

CoRR, 2022

Exact Shape Correspondence via 2D graph convolution.

Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Kaili Ma, Bo Han, Bo Li, James Cheng

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

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.

Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng

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

Understanding and Improving Graph Injection Attack by Promoting Unnoticeability.

Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient.

Shanchao Yang, Kaili Ma, Baoxiang Wang, Hongyuan Zha

CoRR, 2021

Improving Graph Representation Learning by Contrastive Regularization.

Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, James Cheng

CoRR, 2021

HyperGraph Convolution Based Attributed HyperGraph Clustering.

Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

Rethinking Graph Regularization for Graph Neural Networks.

Han Yang, Kaili Ma, James Cheng

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Understanding Graph Neural Networks from Graph Signal Denoising Perspectives.

Guoji Fu, Yifan Hou, Jie Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, James Cheng

CoRR, 2020

Measuring and Improving the Use of Graph Information in Graph Neural Networks.

Yifan Hou, Jie Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang

Proceedings of the 8th International Conference on Learning Representations, 2020