Kaili Ma

Orcid: 0000-0001-9484-8915

Affiliations:
  • Chinese University of Hong Kong, Hong Kong


According to our database1, Kaili Ma authored at least 13 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Online presence:

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Bibliography

2023
GRACE: A General Graph Convolution Framework for Attributed Graph Clustering.
ACM Trans. Knowl. Discov. Data, April, 2023

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Pareto Invariant Risk Minimization.
CoRR, 2022

Invariance Principle Meets Out-of-Distribution Generalization on Graphs.
CoRR, 2022

Exact Shape Correspondence via 2D graph convolution.
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.
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.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient.
CoRR, 2021

Improving Graph Representation Learning by Contrastive Regularization.
CoRR, 2021

HyperGraph Convolution Based Attributed HyperGraph Clustering.
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.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives.
CoRR, 2020

Measuring and Improving the Use of Graph Information in Graph Neural Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020


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