Staff Algorithm Expert @ Ant Group
Email: zhangyalin.gm at gmail dot com
Personal Page: https://sites.google.com/view/yalin
DBLP: https://dblp.uni-trier.de/pid/204/2994.html
Google Scholar: https://scholar.google.com/citations?user=ctmAxP0AAAAJ
Currently, I am a staff algorithm expert in Ant Group, where I have been contributing since earning my master's degree in 2018. My research interests span Large Language Models (LLMs), open-environment machine learning, causal learning, and automated machine learning (AutoML).
I received my M.Sc. degree from Department of Computer Science and Technology , Nanjing University, in 2018, under the supervision of professor Zhi-Hua Zhou. I was a member of LAMDA Group. During that time, I focused on the problem of multi-instance learning and anomaly detection tasks in open environment. I received my B.Sc. degree in School of Computer Science and Engineering in 2015 from Southeast University.
Since 2024, I have been dedicated to LLM R&D within Ant Group’s Ling Team (view our representative models here). My current focus centers on post-training development, particularly Reinforcement Learning. Through these methodologies, I continuously explore the advancement of model reasoning and agentic capabilities.
PS: We are actively hiring! We warmly welcome talents with a strong technical vision to join our team—feel free to reach out to me.
(*=corresponding author，⁺=co-first author)
Ling Team, Caizhi Tang, Chilin Fu, Chunwei Wu, Jia Guo, Jianwen Wang, Jingyu Hu, Liang Jiang, Meng Li, Peng Jiao, Pingping Liu, Shaomian Zheng, Shiwei Liang, Shuaicheng Li, Yalin Zhang, Yingting Wu, Yongkang Liu, Zhenyu Huang. Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models. arXiv preprint arXiv:2504.07158 (2025).
Ling Team, Bin Han, Caizhi Tang, Chen Liang, Donghao Zhang, Fan Yuan, Feng Zhu, Jie Gao, Jingyu Hu, Longfei Li, Meng Li, Mingyang Zhang, Peijie Jiang, Peng Jiao, Qian Zhao, Qingyuan Yang, Wenbo Shen, Xinxing Yang, Yalin Zhang, Yankun Ren, Yao Zhao, Yibo Cao, Yixuan Sun, Yue Zhang, Yuchen Fang, Zibin Lin, Zixuan Cheng, Jun Zhou. Every attention matters: An efficient hybrid architecture for long-context reasoning. arXiv preprint arXiv:2510.19338 (2025).
Q. Shi, J. Zhou*, Y.-L. Zhang, L. Li, C. Ma, Y. Wu, X. Qin. AntAkso: Claims Management System for Health Insurance in Alipay. In: Proceedings of the 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25), Toronto, ON, Canada, August 3-7, 2025, pp. 2536-2547.
B. Han, Y.-X. Sun, Y.-L. Zhang, L. Zhang, H. Hu, L. Li, J. Zhou*, G. Ye, H. He. Collaborative Refining for Learning from Inaccurate Labels. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024.
Y.-X. Sun, Y.-L. Zhang, B. Han, L. Li, J. Zhou*. Self-cognitive Denoising in the Presence of Multiple Noisy Label Sources. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), July 21-27, Vienna, Austria, pp.47261-47279.
B. Han⁺, Y.-L. Zhang⁺, L. Yu, B. Chen, L. Li, J. Zhou*. Modeling Treatment Effect with Cross-Domain Data. In: Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'24), May 7-10, 2024, Taipei, Taiwan, pp.365-377.
Y.-L. Zhang⁺, C. Tang⁺, L. Yu, J. Zhou*, L. Li, Q. Cui, F. Fan, L. Jiang, X. Zhao. Domain Level Interpretability: Interpreting Black-box Model with Domain-specific Embedding. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining(WSDM'24), March 4-8, 2024, Mérida, Mexico, pp.1102-1105. (demo)
Q. Shi, Y.-L. Zhang, L. Yu, F. Zhu, L. Li, J. Zhou, Y. Fang. A Distribution-free Method for Probabilistic Prediction. Expert Systems with Applications, Volume 237, Part B, 2024, 121396.
Y.-X. Sun⁺, Y.-L. Zhang⁺, W. Wang*, L. Li, J. Zhou. Treatment Effect Estimation across Domains. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM'23), October 21-25, 2023, Birmingham, pp.2352-2361.
L. Yu, M. Li, Y.-L. Zhang, L. Li, J. Zhou*. FINRule: Feature Interactive Neural Rule Learning. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM'23), October 21-25, 2023, Birmingham, pp.3020-3029.
H. Lu, H. Qian*, Y. Wu, Z. Liu, Y.-L. Zhang, A. Zhou, Y. Yu. Degradation-Resistant Offline Optimization via Accumulative Risk Control. In: Proceedings of the 26th European Conference on Artificial Intelligence (ECAI'23), September 30-October 5, 2023, Kraków, Poland, pp.1609-1616.
Y.-L. Zhang, J. Zhou*, Y. Ren, Y. Zhang, X. Yang, M. Li, Q. Shi and L. Li. ALT: An Automatic System for Long Tail Scenario Modeling. In: Proceedings of the 39th IEEE International Conference on Data Engineering (ICDE'23), Anaheim, California, USA, 2023, pp.3017-3030.
Y.-L. Zhang⁺, Y.-X. Sun⁺, F. Fan, M. Li, Y.Zhao, W. Wang, L. Li, J. Zhou*, J. Feng. A Framework for Detecting Frauds from Extremely Few Labels. In: Proceedings of the 16th ACM International Conference on Web Search and Data Mining(WSDM'23), February 27-March 3, 2023, Singapore, pp.1124-1127. (demo)
Y.-L. Zhang, J. Zhou*, Q. Shi, L Li. Exploring the combination of self and mutual teaching for tabular-data-related semi-supervised regression. Expert Systems with Applications, Volume 213, Part A, 2023, 118931.
C. Tang⁺, H. Wang⁺, X. Li, Q. Cui, Y.-L. Zhang, F. Zhu, L. Li, J. Zhou*, L. Jiang. Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), New Orleans, Louisiana, 2022, pp.5628-5640.
Y. Zhang, X. Yang, F. Zhu, Y.-L. Zhang, M. Li, Q. Shi, L. Li, J. Zhou. A Task-Aware Attention-Based Method for Improved Meta-Learning. In: Proceedings of the 6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM'22). Springer, Cham, Nanjing, China, November 25-27, 2022, pp.474-482.
M. Li⁺, L. Yu⁺, Y.-L. Zhang, X. Huang, Q. Shi, Q. Cui, X. Yang, L. Li, W. Zhu, Y. Fang, J. Zhou*. An Adaptive Framework for Confidence-constraint Rule Set Learning Algorithm in Large Dataset. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM'22), October 17-21, 2022, Hybrid Conference, Hosted in Atlanta, Georgia, USA, pp.3252-3261. (Best paper candidate)
Q. Yu, S. Yang, Z. Zhang, Y.-L. Zhang, B. Hu, Z. Liu, K. Huang, X. Zhong, J. Zhou, Y. Fang. A Graph Attention Network Model for GMV Forecast on Online Shopping Festival. In: Proceedings of the 5th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM'21). Springer, Cham, Guangzhou, China, August 23-25, 2021, pp.134-139.
Y.-L. Zhang, Q. Shi, M. Li, X. Yang, L. Li, J. Zhou*. A Classification based Ensemble Pruning Framework with Multi-metric Consideration. In: Proceedings of SAI Intelligent Systems Conference, IntelliSys 2021. Springer, Cham, Amsterdam, The Netherlands, September 2-3, 2021, pp.650-667.
M. Li, Y.-L. Zhang, Q. Shi, X. Yang, Q. Cui, L. Li, J. Zhou*. Constraint-Adaptive Rule Mining in Large Databases. In: Proceedings of the 26th International Conference on Database Systems for Advanced Applications (DASFAA'21), April 11-14, 2021, Taipei, Taiwan, pp.579-591.
K. Xu, C. Fu, X. Zhang, C. Chen, Y.-L. Zhang, W. Rong, Z. Wen, J. Zhou, Y. Qiao. aDMSCN: A Novel Perspective for User Intent Prediction in Customer Service Bots. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM'20), October19-23, 2020, Virtual Event, Ireland, pp. 2853-2860.
C. Chen⁺, Y.-L. Zhang⁺, M. Qiu, B. Wu, L. Wang, L. Li and J. Zhou. Automatic Knowledge Fusion in Transferrable Networks for Semantic Text Matching. In: Companion Proceedings of the Web Conference 2020 (WWW'20), April 20-24, 2020, Taipei, Taiwan, pp.73-74. (poster)
Q. Shi, Y.-L. Zhang*, L. Li, X. Yang, M. Li and J. Zhou. SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks. In: Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE'20), Dallas, Texas, 2020, pp.1645-1656.
Y.-L. Zhang⁺ and L. Li⁺. Interpretable MTL from Heterogeneous Domains using Boosted Tree. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM'19), November 3-7, 2019, Beijing, China, pp.2053-2056.
Y.-L. Zhang, J. Zhou, W. Zheng, J. Feng, L. Li, Z. Liu, M. Li, Z. Zhang, C. Chen, X. Li, Y. Qi and Z.-H. Zhou. Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud . ACM Transactions on Intelligent Systems and Technology, 10, 5 , Article 55 (September 2019), 19 pages.
Y.-L. Zhang, L. Li, J. Zhou, X. Li and Z.-H. Zhou. Anomaly Detection with Partially Observed Anomalies. In: Proceedings of the International Conference on World Wide Web Companion (WWW'18), Lyon, France, 2018, pp.639-646.
Y.-L. Zhang, L. Li, J. Zhou, X. Li, Y. Liu, Y. Zhang, and Z.-H. Zhou. A PU learning based system for potential malicious URL detection. In: Proceedings of the 24th ACM SIGSAC Conference on Computer and Communications Security (CCS'17), Dallas, TX, 2017, pp.2599-2601. (poster).
Y.-L. Zhang and Z.-H. Zhou. Multi-instance learning with key instance shift. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3441-3447.
2020年CCF科学技术奖科技进步卓越奖, （项目成员）
National Graduate Scholarship (Nanjing University), 2017
Google Scholarship, 2014
Meritorious Winner in National College Mathematical Contest in Modeling(MCM), 2014
Second Prize in China Undergraduate Mathematical Contest in Modeling(CUMCM), 2013
National Undergraduate Scholarship (Southeast University), 2013
Presidential Scholarship (Southeast University), 2012