Kaili Ma

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 19

Top Topics

Risk Minimization

Graph Clustering

Top Venues

Trans. Mach. Learn. Res.

Publications

Kaili Ma, Han Yang, Shanchao Yang, Kangfei Zhao, Lanqing Li, Yongqiang Chen, Junzhou Huang, James Cheng, Yu Rong
Solving the non-submodular network collapse problems via Decision Transformer. Neural Networks 176 (2024)
Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han
GRACE: A General Graph Convolution Framework for Attributed Graph Clustering. ACM Trans. Knowl. Discov. Data 17 (3) (2023)
Kaili Ma, Garry Yang, Han Yang, Yongqiang Chen, James Cheng
Calibrating and Improving Graph Contrastive Learning. Trans. Mach. Learn. Res. 2023 (2023)
Shanchao Yang, Kaili Ma, Baoxiang Wang, Tianshu Yu, Hongyuan Zha
Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring. Trans. Mach. Learn. Res. 2023 (2023)
Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. ICLR (2023)
Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Kaili Ma, Bo Han, Bo Li, James Cheng
Exact Shape Correspondence via 2D graph convolution. NeurIPS (2022)
Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang
Measuring and Improving the Use of Graph Information in Graph Neural Networks. CoRR (2022)
Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR (2022)
Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng
Pareto Invariant Risk Minimization. CoRR (2022)
Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS (2022)
Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR (2022)
Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng
Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR (2022)
Han Yang, Kaili Ma, James Cheng
Rethinking Graph Regularization for Graph Neural Networks. AAAI (2021)
Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han
HyperGraph Convolution Based Attributed HyperGraph Clustering. CIKM (2021)
Shanchao Yang, Kaili Ma, Baoxiang Wang, Hongyuan Zha
Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient. CoRR (2021)
Kaili Ma, Haochen Yang, Han Yang, Tatiana Jin, Pengfei Chen, Yongqiang Chen, Barakeel Fanseu Kamhoua, James Cheng
Improving Graph Representation Learning by Contrastive Regularization. CoRR (2021)
Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang
Measuring and Improving the Use of Graph Information in Graph Neural Networks. ICLR (2020)
Guoji Fu, Yifan Hou, Jian Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, James Cheng
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives. CoRR (2020)
Han Yang, Kaili Ma, James Cheng
Rethinking Graph Regularization For Graph Neural Networks. CoRR (2020)