Biography

Currently, I am an algorithm expert in Ant Group, having joined the company after graduating with my master's degree in 2018. I am with a wide interest on open-environment machine learning, causal learning, and automatic machine learning etc.   Recently,  I'm focusing on some typical fields of open-environment machine learning, i.e., towards robust/improved modeling from imperfect datasets, and trying to explore these techniques to solve business tasks.

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. 

Publications: 

(*=corresponding author，⁺=co-first author)

1. 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.

2. 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)

3. 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. 

4. 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.

5. 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.

6. 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.

7. 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.

8. 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)

9. 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. 

10. 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 2022), New Orleans, Louisiana, 2022, pp.5628-5640. 

11. 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.

12. 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)

13. 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.

14. 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.

15. 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. 

16. 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.

17. 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)

18. 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.

19. 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.

20. 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.

21. 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.

22. 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).

23. 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.

Selected Awards: 

• 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