Han Yang 杨晗
I am an AI Tech Lead at Kaito.ai. I obtained my Ph.D. in 2023 from the Department of Computer Science & Engineering at The Chinese University of Hong Kong, advised by Prof. James Cheng. Prior to that, I received my B.Eng. in 2019 from Chu Kochen Honors College, Zhejiang University.
Publications
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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
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Calibrating and Improving Graph Contrastive Learning
Kaili Ma, Garry Yang, Han Yang, Yongqiang Chen, James Cheng
TMLR, 2023
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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
ICLR, 2023
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Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng
ICLR, 2022
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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
NeurIPS, 2022
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Exact Shape Correspondence via 2D graph convolution
Barakeel Fanseu Kamhoua, Lin Zhang, Yongqiang Chen, Han Yang, Kaili Ma, Bo Han, Bo Li, James Cheng
NeurIPS, 2022
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HGL: Accelerating Heterogeneous GNN Training with Holistic Representation and Optimization
Yuntao Gui, Yidi Wu, Han Yang, Tatiana Jin, Boyang Li, Qihui Zhou, James Cheng, Fan Yu
SC, 2022
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Rethinking Graph Regularization for Graph Neural Networks
Han Yang, Kaili Ma, James Cheng
AAAI, 2021
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Self-Enhanced GNN: Improving Graph Neural Networks using Model Outputs
Han Yang, Xiao Yan, Xinyan Dai, Yongqiang Chen, James Cheng
IJCNN, 2021
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Convolutional Embedding for Edit Distance
Xinyan Dai, Xiao Yan, Kaiwen Zhou, Yuxuan Wang, Han Yang, James Cheng
SIGIR, 2020