Yijun Tian

According to our database, Yijun Tian authored at least 43 papers between 2020 and 2024.

Collaborative distances:

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

2024

Advancing crowd forecasting with graphs across microscopic trajectory to macroscopic dynamics.

Chuan-Zhi Thomas Xie, Junhao Xu, Bin Zhu, Tie-Qiao Tang, Siuming Lo, Botao Zhang, Yijun Tian

Inf. Fusion, 2024

Machine Unlearning in Generative AI: A Survey.

Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, Meng Jiang

CoRR, 2024

Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas.

Chengyuan Deng, Yiqun Duan, Xin Jin, Heng Chang, Yijun Tian, Han Liu, Henry Peng Zou, Yiqiao Jin, Yijia Xiao, Yichen Wang, Shenghao Wu, Zongxing Xie, Kuofeng Gao, Sihong He, Jun Zhuang, Lu Cheng, Haohan Wang

CoRR, 2024

Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing.

Yuying Duan, Yijun Tian, Nitesh V. Chawla, Michael Lemmon

CoRR, 2024

Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning.

Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V. Chawla, Stan Z. Li

CoRR, 2024

UGMAE: A Unified Framework for Graph Masked Autoencoders.

Yijun Tian, Chuxu Zhang, Ziyi Kou, Zheyuan Liu, Xiangliang Zhang, Nitesh V. Chawla

CoRR, 2024

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering.

Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan Hooi

CoRR, 2024

TinyLLM: Learning a Small Student from Multiple Large Language Models.

Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang, Nitesh V. Chawla

CoRR, 2024

Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies.

Lincan Li, Wei Shao, Wei Dong, Yijun Tian, Qiming Zhang, Kaixiang Yang, Wenjie Zhang

CoRR, 2024

Structural Podcast Content Modeling with Generalizability.

Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo, Hugues Bouchard

Proceedings of the Companion Proceedings of the ACM on Web Conference 2024, 2024

Can we Soft Prompt LLMs for Graph Learning Tasks?

Zheyuan Liu, Xiaoxin He, Yijun Tian, Nitesh V. Chawla

Proceedings of the Companion Proceedings of the ACM on Web Conference 2024, 2024

Breaking the Trilemma of Privacy, Utility, and Efficiency via Controllable Machine Unlearning.

Zheyuan Liu, Guangyao Dou, Eli Chien, Chunhui Zhang, Yijun Tian, Ziwei Zhu

Proceedings of the ACM on Web Conference 2024, 2024

Graph Cross Supervised Learning via Generalized Knowledge.

Xiangchi Yuan, Yijun Tian, Chunhui Zhang, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning.

Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V. Chawla, Stan Z. Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning.

Guancheng Wan, Yijun Tian, Wenke Huang, Nitesh V. Chawla, Mang Ye

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mitigating Emergent Robustness Degradation while Scaling Graph Learning.

Xiangchi Yuan, Chunhui Zhang, Yijun Tian, Yanfang Ye, Chuxu Zhang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding.

Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V. Chawla, Stan Z. Li

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning.

Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, Zheyuan Liu, Bing Yin, Meng Jiang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation.

Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesh V. Chawla

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

FaDE: A Face Segment Driven Identity Anonymization Framework For Fair Face Recognition.

Ziyi Kou, Yijun Tian, Meng Jiang, Xiangliang Zhang

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

Towards Safer Large Language Models through Machine Unlearning.

Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, Meng Jiang

Proceedings of the Findings of the Association for Computational Linguistics, 2024

Graph Neural Prompting with Large Language Models.

Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning.

Zheyuan Liu, Guangyao Dou, Yijun Tian, Chunhui Zhang, Eli Chien, Ziwei Zhu

CoRR, 2023

Class-Imbalanced Learning on Graphs: A Survey.

Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla

CoRR, 2023

Knowledge Distillation on Graphs: A Survey.

Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh V. Chawla

CoRR, 2023

Fair Graph Representation Learning via Diverse Mixture-of-Experts.

Zheyuan Liu, Chunhui Zhang, Yijun Tian, Erchi Zhang, Chao Huang, Yanfang Ye, Chuxu Zhang

Proceedings of the ACM Web Conference 2023, 2023

Character As Pixels: A Controllable Prompt Adversarial Attacking Framework for Black-Box Text Guided Image Generation Models.

Ziyi Kou, Shichao Pei, Yijun Tian, Xiangliang Zhang

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Graph-based Molecular Representation Learning.

Zhichun Guo, Kehan Guo, Bozhao Nan, Yijun Tian, Roshni G. Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh V. Chawla

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations.

Chunhui Zhang, Chao Huang, Yijun Tian, Qianlong Wen, Zhongyu Ouyang, Youhuan Li, Yanfang Ye, Chuxu Zhang

Proceedings of the International Conference on Machine Learning, 2023

Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization.

Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency.

Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Boosting Graph Neural Networks via Adaptive Knowledge Distillation.

Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Heterogeneous Graph Masked Autoencoders.

Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning.

Chunhui Zhang, Chao Huang, Yijun Tian, Qianlong Wen, Zhongyu Ouyang, Youhuan Li, Yanfang Ye, Chuxu Zhang

CoRR, 2022

NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs.

Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla

CoRR, 2022

Graph-based Molecular Representation Learning.

Zhichun Guo, Bozhao Nan, Yijun Tian, Olaf Wiest, Chuxu Zhang, Nitesh V. Chawla

CoRR, 2022

FakeEdge: Alleviate Dataset Shift in Link Prediction.

Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla

Proceedings of the Learning on Graphs Conference, 2022

Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks.

Yijun Tian, Chuxu Zhang, Zhichun Guo, Yihong Ma, Ronald A. Metoyer, Nitesh V. Chawla

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation.

Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting.

Yihong Ma, Patrick Gérard, Yijun Tian, Zhichun Guo, Nitesh V. Chawla

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Recipe Recommendation With Hierarchical Graph Attention Network.

Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla

Frontiers Big Data, 2021

Recipe Representation Learning with Networks.

Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla

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

2020

Quasi-Experimental Designs for Assessing Response on Social Media to Policy Changes.

Yijun Tian, Rumi Chunara

Proceedings of the Fourteenth International AAAI Conference on Web and Social Media, 2020