Kexin Zhang

According to our database, Kexin Zhang authored at least 17 papers between 2023 and 2025.

Collaborative distances:

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

2025

From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptation.

Rongyao Cai, Ming Jin, Qingsong Wen, Kexin Zhang

CoRR, July, 2025

Cross-Domain Conditional Diffusion Models for Time Series Imputation.

Kexin Zhang, Baoyu Jing, K. Selçuk Candan, Dawei Zhou, Qingsong Wen, Han Liu, Kaize Ding

CoRR, June, 2025

A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization.

Ziqing Wang, Kexin Zhang, Zihan Zhao, Yibo Wen, Abhishek Pandey, Han Liu, Kaize Ding

CoRR, May, 2025

Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models.

Kexin Zhang, Fuyuan Lyu, Xing Tang, Dugang Liu, Chen Ma, Kaize Ding, Xiuqiang He, Xue Liu

Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025

Combinatorial Optimization Perspective based Framework for Multi-behavior Recommendation.

Chenhao Zhai, Chang Meng, Yu Yang, Kexin Zhang, Xuhao Zhao, Xiu Li

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

2024

Debiased Contrastive Learning With Supervision Guidance for Industrial Fault Detection.

Rongyao Cai, Wang Gao, Linpeng Peng, Zhengming Lu, Kexin Zhang, Yong Liu

IEEE Trans. Ind. Informatics, November, 2024

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.

Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Y. Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan

IEEE Trans. Pattern Anal. Mach. Intell., October, 2024

Debiased Contrastive Learning for Time-Series Representation Learning and Fault Detection.

Kexin Zhang, Rongyao Cai, Chunlin Zhou, Yong Liu

IEEE Trans. Ind. Informatics, May, 2024

A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation.

Kexin Zhang, Shuhan Liu, Song Wang, Weili Shi, Chen Chen, Pan Li, Sheng Li, Jundong Li, Kaize Ding

CoRR, 2024

Position Paper: What Can Large Language Models Tell Us about Time Series Analysis.

Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen

CoRR, 2024

IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation.

Kexin Zhang, Yichao Wang, Xiu Li, Ruiming Tang, Rui Zhang

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Position: What Can Large Language Models Tell Us about Time Series Analysis.

Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen

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

Skip-Step Contrastive Predictive Coding for Time Series Anomaly Detection.

Kexin Zhang, Qingsong Wen, Chaoli Zhang, Liang Sun, Yong Liu

Proceedings of the IEEE International Conference on Acoustics, 2024

DCS: Debiased Contrastive Learning with Weak Supervision for Time Series Classification.

Rongyao Cai, Linpeng Peng, Zhengming Lu, Kexin Zhang, Yong Liu

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Expert demonstrations guide reward decomposition for multi-agent cooperation.

Weiwei Liu, Jing Wei, Shanqi Liu, Yudi Ruan, Kexin Zhang, Yang Jiang, Yong Liu

Neural Comput. Appl., September, 2023

Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.

Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong Liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan

CoRR, 2023

HCL4QC: Incorporating Hierarchical Category Structures Into Contrastive Learning for E-commerce Query Classification.

Lvxing Zhu, Kexin Zhang, Hao Chen, Chao Wei, Weiru Zhang, Haihong Tang, Xiu Li

Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023