I am an Assistant Researcher in College of Computer Science & Software Engineering at Hohai University. I obtained my Ph.D. degree from Department of Computer Science & Technology in Nanjing University in Dec. 2022, where I was very fortunate to be advised by Prof. Zhi-Hua Zhou. Before that, I received my B.Sc. degree from Department of Statistics in University of Science and Technology of China in Jun. 2017.
My research interest includes ensemble learning and learning theory.
[Resume] & [中文简历]
🔥 News
- Enrolling Students: Looking for self-motivated M.Sc/Ph.D. students to work on Artificial Intelligence.
Feel free to send me an email with your resume and a document stating your research motivation. - 2024.05: 🎉🎉 Our paper “Confidence-aware Contrastive Learning for Selective Classification” is accepted by the CCF-A international conference ICML 2024.
- 2023.12: 🎉🎉 My doctoral dissertation “Research on Theoretical Analysis of Deep Forests and Extensions” was awarded the Excellent Doctoral Dissertation of Jiangsu Artificial Intelligence Society.
- 2022.12: 🎉🎉 Our paper “Depth is More Powerful than Width with Prediction Concatenation in Deep Forests” is accepted by the CCF-A international conference NeurIPS 2022 as an Oral Representation.
- 2019.12: 🎉🎉 Our paper “A Refined Margin Distribution Analysis for Forest Representation Learning” is accepted by the CCF-A international conference NeurIPS 2019.
👨💻 Students
M.Eng Students
2023: [Tian-Shuang Wu 吴填双]; [Ning Chen 陈宁];
Collected Seminars
[ML&DM Seminar]: Seminar on Machine Learning and Data Mining for my students.
[FAI Seminar]: International Seminar on Foundational Artificial Intelligence.
📝 Publications
- [ICML 2024] Confidence-aware Contrastive Learning for Selective Classification. [paper] [code] [bib]
Yu-Chang Wu, Shen-Huan Lyu, Haopu Shang, Xiangyu Wang, and Chao Qian.
In: Proceedings of the 41st International Conference on Machine Learning, in press, 2024.
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[IWQoS 2024] Identifying Key Tag Distribution in Large-Scale RFID Systems. [paper] [code] [bib]
Yanyan Wang, Jia Liu, Shen-Huan Lyu, Zhihao Qu, Bin Tang, and Baoliu Ye.
In: IEEE/ACM 32nd International Symposium on Quality of Service, in press, 2024. -
[TKDD 2024] Interpreting Deep Forest through Feature Contribution and MDI Feature Importance. [paper] [code] [bib]
Yi-Xiao He, Shen-Huan Lyu, and Yuan Jiang.
ACM Transactions on Knowledge Discovery from Data, in press, 2024. -
[JOS 2024] Interaction Representations Based Deep Forest Method in Multi-Label Learning. [paper] [code] [bib]
Shen-Huan Lyu, Yi-He Chen, and Yuan Jiang.
Journal of Software, 35(4):1934-1944, 2024. -
[AISTATS 2023] On the Consistency Rate of Decision Tree Learning Algorithms. [paper] [code] [bib]
Qin-Cheng Zheng, Shen-Huan Lyu, Shao-Qun Zhang, Yuan Jiang, and Zhi-Hua Zhou.
In: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics, pp. 7824-7848, Valencia, ES, 2023.
- [NeurIPS 2022 Oral] Depth is More Powerful than Width with Prediction Concatenation in Deep Forests. [paper] [code] [bib]
Shen-Huan Lyu, Yi-Xiao He, and Zhi-Hua Zhou.
In: Advances in Neural Information Processing Systems 35, pp. 29719-29732, New Orleans, Louisiana, US, 2022.
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[NN 2022] Improving Generalization of Neural Networks by Leveraging Margin Distribution. [paper] [code] [bib]
Shen-Huan Lyu, Lu Wang, and Zhi-Hua Zhou.
Neural Networks, 151:48-60, 2022. -
[CJE 2022] A Region-Based Analysis for the Feature Concatenation in Deep Forests. [paper] [code] [bib]
Shen-Huan Lyu, Yi-He Chen, and Zhi-Hua Zhou.
Chinese Journal of Electronics, 31(6):1072-1080, 2022. -
[ICDM 2021] Improving Deep Forest by Exploiting High-Order Interactions. [paper] [code] [bib]
Yi-He Chen*, Shen-Huan Lyu*, and Yuan Jiang (* indicates equal contribution).
In: Proceedings of the 21st IEEE International Conference on Data Mining, pp. 1030-1035, Auckland, NZ, 2021.
- [NeurIPS 2019] A Refined Margin Distribution Analysis for Forest Representation Learning. [paper] [code] [bib]
Shen-Huan Lyu, Liang Yang, and Zhi-Hua Zhou.
In: Advances in Neural Information Processing Systems 32, pp. 5531-5541, Vancouver, British Columbia, CA, 2019.
🎖 Honors and Awards
- 2023.12 Excellent Doctoral Dissertation of Jiangsu Artificial Intelligence Society, Jiangsu.
- 2023.06 The 5th Special Funding from China Postdoctoral Science Foundation, China.
- 2022.12 Jiangsu Excellent Postdoctoral Program, Jiangsu.
- 2019.10 Artificial Intelligence Scholarship in Nanjing University, Nanjing.
- 2019.09 The First Class Academic Scholarship in Nanjing University, Nanjing.
- 2018.09 The First Class Academic Scholarship in Nanjing University, Nanjing.
- 2017.09 Presidential Special Scholarship for first year Ph.D. Student in Nanjing University, Nanjing.
- 2016.09 The Silver Prize Scholarship for Excellent Student in University of Science and Technology of China, Hefei.
✨ Academic Service
Senior Program Committee Member of Conferences:
- IJCAI: 2021
Program Committee Member of Conferences:
- ICML: 2021-2024
- NeurIPS: 2020-2023
- AAAI: 2020, 2021, 2023
- IJCAI: 2020, 2022-2024
- ICLR: 2020-2023
Reviewer for Journals:
- Artificial Intelligence (AIJ)
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
- ACM Transactions on Knowledge Discovery from Data (TKDD)
- Machine Learning (MLJ)
- Research
- Chinese Journal of Electronics (CJE)
- 软件学报 (Journal of Software, JOS)
📖 Educations
- 2017.09 - 2022.12, Ph.D. in Computer Science, Nanjing University (NJU)
- 2013.09 - 2017.06, B.Sc. in Statistics, University of Science and Technology of China (USTC)
💬 Invited Talks
- 2023.11, Deep Forest, Z-Park National Laboratory, Beijing.
- 2022.12, Depth is More Powerful than Width, New Orleans Convention Center, Online.
- 2022.01, Margin Distribution Neural Networks, Huawei Noah’s Ark Lab, Online.
💻 Experiences
- 2022.12 - now, Assistent Researcher in Hohai University, Nanjing.
- 2022.06 - 2022.08, Machine Learning Engineer in Huawei Noah’s Ark Lab, Nanjing.