Preprint

• MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
  Kaixiong Zhou*, Cameron Diao*, Xiao Huang, Xia Hu.
  
• A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
  Xuansheng Wu*, Kaixiong Zhou*, Mingchen Sun, Xin Wang, Ninghao Liu.
  
• Editable Graph Neural Network for Node Classifications
  Zirui Liu, Zhimeng Jiang, Shaochen Zhong, Kaixiong Zhou, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu.
  
• PokeMQA: Programmable knowledge editing for Multi-hop Question Answering
  Hengrui Gu, Kaixiong Zhou, Xiaotian Han, Ninghao Liu, Ruobing Wang, Xin Wang.
  

Book Chapter

• Graph Neural Networks: AutoML
  Kaixiong Zhou, Zirui Liu, Keyu Duan, Xia Hu
  Graph Neural Networks: Foundations, Frontiers, and Applications
  

Conference/Journal Papers (* indicates equal contributions)

• Knowledge Graphs Can be Learned with Just Intersection Features
  Duy Le, Shaochen Zhong, Zirui Liu, Shuai Xu, Vipin Chaudhary, Kaixiong Zhou, Zhaozhuo Xu.
  International Conference on Machine Learning (ICML), 2024.
  
• Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
  Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu, Ying Wang, Xin Wang.
  International Conference on Machine Learning (ICML), 2024.
  
• GNNs Also Deserve Editing, and They Need It More Than Once
  Shaochen Zhong, Duy Le, Zirui Liu, Zhimeng Jiang, Andrew Ye, Jiamu Zhang, Jiayi Yuan, Kaixiong Zhou, Zhaozhuo Xu, Jing Ma, Shuai Xu, Vipin Chaudhary, Xia Hu.
  International Conference on Machine Learning (ICML), 2024.
  
• Soft Prompt Recovers Compressed LLMs, Transferably
  Zhaozhuo Xu, Zirui Liu, Beidi Chen, Yuxin Tang, Jue Wang, Kaixiong Zhou, Xia Hu, Anshumali Shrivastava.
  International Conference on Machine Learning (ICML), 2024.
  
• TVE: Learning Meta-attribution for Transferable Vision Explainer
  Guanchu Wang, Yu-Neng Chuang, Fan Yang, Mengnan Du, Chia-Yuan Chang, Shaochen Zhong, Zirui Liu, Zhaozhuo Xu, Kaixiong Zhou, Xuanting Cai, Xia Hu
  International Conference on Machine Learning (ICML), 2024.
  
• Few-shot Time Series Classification via Sharpness Aware Minimization (workshop paper)
  Jesus Barreda, Ruben Puga, Kaixiong Zhou, Li Zhang.
  AI4TS Workshop in SIAM Conference on Data Mining, 2024.
  
• Molecular Data Programming: Towards Molecule Pseudo-labeling with Systematic Weak Supervision
  Xin Juan, Kaixiong Zhou, Ninghao Liu, Tianlong Chen, Xin Wang.
  Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  
• Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models
  Yili Wang, Kaixiong Zhou, Ninghao Liu, Ying Wang, Xin Wang.
  International Conference on Learning Representation (ICLR), 2024.
  
• Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
  Zirui Liu, Guanchu Wang, Shaochen Zhong, Zhaozhuo Xu, Daochen Zha, Ruixiang Tang, Zhimeng Jiang, Kaixiong Zhou, Vipin Chaudhary, Shuai Xu, Xia Hu.
  Conference on Neural Information Processing Systems (NeurIPS), 2023.
  
• LeanFlex-GKP: Advancing Hassle-Free Structured Pruning with Simple Flexible Group Count
  Jiamu Zhang, Shaochen Zhong, Andrew Ye, Zirui Liu, Kaixiong Zhou, Xia Hu, Shuai Xu, Vipin Chaudhary.
  Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@NeurIPS), 2023.
  
• DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research (demo paper)
  Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Kwei-Herng Lai, Daochen Zha, Ruixiang Tang, Fan Yang, Alfredo Costilla Reyes, Kaixiong Zhou, Xiaoqian Jiang, Xia Hu.
  ACM International Conference on Information and Knowledge Management (CIKM), 2023.
  
• DSpar: An Embarrassingly Simple Strategy for Efficient GNN training and inference via Degree-based Sparsification
  Zirui Liu, Kaixiong Zhou, Zhimeng Jiang, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu.
  Transactions on Machine Learning Research (TMLR), 2023.
  
• Adaptive RiskAware Bidding with Budget Constraint in Display Advertising
  Zhimeng Jiang, Kaixiong Zhou, Mi Zhang, Rui Chen, Xia Hu, Soo-Hyun Choi.
  SIGKDD Explorations, 2023
  
• Marginal Nodes Matter: Towards Structure Fairness in Graphs
  Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang, Jundong Li, Fei Wang, Na Zou.
  SIGKDD Explorations, 2023
  
• Hessian-aware Quantized Node Embeddings for Recommendation (short paper)
  Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Chin-Chia Michael Yeh, Yan Zheng, Xia Hu, Hao Yang.
  ACM Recommender Systems Conference (RecSys), 2023.
  
• ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
  Yucheng Shi, Kaixiong Zhou, Ninghao Liu.
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2023.
  
• RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
  Zirui Liu, Shengyuan Chen, Kaixiong Zhou, Daochen Zha, Xiao Huang, Xia Hu.
  International Conference on Machine Learning (ICML), 2023.
  
• Probabilistic Masked Attention Networks for Explainable Sequential Recommendation
  Huiyuan Chen, Kaixiong Zhou, Zhimeng Jiang, Michael Yeh, Xiaoting Li, Menghai Pan, Yan Zheng, Xia Hu, Hao Yang.
  International Joint Conference on Artificial Intelligence (IJCAI), 2023.
  
• Adaptive Label Smoothing To Regularize Large-Scale Graph Training
  Kaixiong Zhou*, Soo-Hyun Choi*, Zirui Liu, Ninghao Liu, Fan Yang, Rui Chen, Li Li, Xia Hu.
  SIAM International Conference on Data Mining (SDM), 2023.
  
• Context-aware Domain Adaptation for Time Series Anomaly Detection
  Kwei-Herng Lai, Lan Wang, Huiyuan Chen, Kaixiong Zhou, Fei Wang, Hao Yang, Xia Hu.
  SIAM International Conference on Data Mining (SDM), 2023.
  
• Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising (workshop paper)
  Zhimeng Jiang, Kaixiong Zhou, Mi Zhang, Rui Chen, Xia Hu, Soo-Hyun Choi.
  AI for Web Advertising Workshop in AAAI 2023.
  
• Auto-GNN: Neural Architecture Search of Graph Neural Networksbr
  Kaixiong Zhou, Xiao Huang, Qingquan Song, Rui Chen, Xia Hu.
  Frontiers in Big Data-Machine Learning and Artificial Intelligence
  
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
  Kaixiong Zhou, Zhenyu Zhang, Shengyuan Chen, Tianlong Chen, Xiao Huang, Zhangyang Wang, and Xia Hu.
  Quantum Techniques in Machine Learning (QTML), 2022.
  
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
  Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, Zhangyang Wang.
  Conference on Neural Information Processing Systems (NeurIPS), 2022.
  
• AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training
  Kaixiong Zhou*, Yili Wang*, Rui Miao, Ninghao Liu, Xin Wang.
  ACM International Conference on Information and Knowledge Management (CIKM), 2022.
  
• TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
  Huiyuan Chen, Xiaoting Li, Kaixiong Zhou, Xia Hu, Chin-Chia Michael Yeh, Yan Zheng, Hao Yang.
  ACM Recommender Systems Conference (RecSys), 2022.
  
• GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks
  Kaixiong Zhou*, Ming-chen Sun*, Xin He, Ying Wang, Xin Wang.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022.
  
• Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
  Kaixiong Zhou*, Tianlong Chen*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
  
• Table2Graph: Transforming Tabular Data to Unified Weighted Graph
  Kaixiong Zhou, Zirui Liu, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu.
  International Joint Conference on Artificial Intelligence (IJCAI), 2022.
  
• AutoCoG: A Unified Data-Model Co-Search Framework for Graph Neural Networks
  Duc N.M Hoang, Kaixiong Zhou, Tianlong Chen, Xia Hu, and Zhangyang Wang.
  International Conference on Automated Machine Learning (AutoML-Conf), 2022.
  
• Adversarial Graph Perturbations for Recommendations at Scale (short paper)
  Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Xia Hu, Fei Wang, and Hao Yang.
  International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022.
  
• Benchmarking Large-Scale Graph Training Over Effectiveness And Efficiency (workshop paper)
  Keyu Duan, Zirui Liu, Wenqing Zheng, Peihao Wang, Kaixiong Zhou, Tianlong Chen, Zhangyang Wang, and Xia Hu.
  Workshop of the Graph Learning Benchmarks of The Web Conference, 2022.
  
• EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
  Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, and Xia Hu.
  International Conference on Learning Representation (ICLR), 2022.
  
• An Information Fusion Approach to Learning with Instance-Dependent Label Noise
  Zhimeng Jiang, Kaixiong Zhou, Zirui Liu, Li Li, Rui Chen, Soo-Hyun Choi, and Xia Hu.
  International Conference on Learning Representation (ICLR), 2022.
  
• Towards Similarity-Aware Time-Series Classification with Graph Neural Networks
  Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, and Xia Hu.
  SIAM International Conference on Data Mining (SDM), 2022.
  
• Orthogonal Graph Neural Networks
  Kai Guo, Kaixiong Zhou, Xia Hu, Yu Li, Yi Chang, Xin Wang.
  AAAI Conference on Artificial Intelligence (AAAI), 2022.
  
• Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
  Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu.
  Conference on Neural Information Processing Systems (NeurIPS), 2021.
  
• DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization
  Zirui Liu, Haifeng Jin, Ting-Hsiang Wang, Kaixiong Zhou, and Xia Hu.
  International Conference on Computer Vision (ICCV), 2021.
  
• AutoAD: Automated Anomaly Detection via Curiosity-guided Search and Self-imitation Learning
  Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and Xia Hu.
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021.
  
• Temporal Augmented Graph Neural Networks for Session-Based Recommendations (short paper)
  Huachi Zhou, Qiaoyu Tan, Xiao Huang, Kaixiong Zhou, and Xiaoling Wang.
  International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2021.
  
• Towards Deeper Graph Neural Networks with Differentiable Group Normalization
  Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, and Xia Hu.
  Conference on Neural Information Processing Systems (NeurIPS), 2020.
  
• Detecting Interactions from Neural Networks via Topological Analysis
  Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Xia Hu.
  Conference on Neural Information Processing Systems (NeurIPS), 2020.
  
• Neural Architecture Search for Outlier Detction (short paper)
  Yuening Li, Zhengzhang Chen, Daochen Zha, Kaixiong Zhou, Haifeng Jin, Haifeng Chen, and Xia Hu.
  International Conference on Data Engineering (ICDE), 2020.
  
• Aggregation Optimization for Graph Neural Networks
  Kwei Herng Lai, Daochen Zha, Kaixiong Zhou, and Xia Hu.
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2020.
  
• Multi-Channel Graph Convolutional Networks
  Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, Xia Hu.
  International Joint Conference on Artificial Intelligence (IJCAI), 2020.
  
• Deep Neural Networks with Knowledge Instillation
  Fan Yang, Ninghao Liu, Mengnan Du, Kaixiong Zhou, Shuiwang Ji, and Xia Hu.
  SIAM International Conference on Data Mining (SDM), 2020.
  
• Experience Replay Optimization
  Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, and Xia Hu.
  International Joint Conference on Artificial Intelligence (IJCAI), 2019.
  
• Distributed Channel Allocation and Rate Control for Hybrid FSO/RF Vehicular Ad Hoc Networks
  Kaixiong Zhou, Chen Gong, Nan Wu, Zhengyuan Xu.
  IEEE/OSA Journal of Optical Communications and Networking (JOCN), 2017.
  
• Color Planning and Inter-Cell Interference Coordination for Multi-Color Visible Light Communication Networks
  Kaixiong Zhou, Chen Gong, Zhengyuan Xu
  IEEE/OSA Journal of Lightwave Technology (JLT), 2017.
  
• Design and Demonstration of An Indoor Visible Light Communication Network with Dynamic User Access and Resource Allocation
  Mian Zeng, Kaixiong Zhou, Chen Gong, Shun Lou, Xianqing Jin, Zhengyuan Xu.
  IEEE International Conference on Wireless Communication and Signal Processing (WCSP), 2017.
  
• Inter-Cell Interference Coordination for Multi-Color Visible Light Communication Networks
  Kaixiong Zhou, Chen Gong, Qian Gao, Zhengyuan Xu.
  IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016.
  
• Enhanced Effective SNR Prediction for LTE Downlink
  Kaixiong Zhou, Lin Zhang, and Ming Jiang.
  IEEE International Conference in Communication in China (ICCC), 2015.