About Me
Hi! I’m Shilong Bao(包世龙, E-mail: baoshilong@ucas.ac.cn). Now I am a Post-doc Fellow with the School of Computer Science and Technology, University of Chinese Academy of Sciences (UCAS). I got my Ph.D. degree in Institute of Information Engineering, Chinese Academy of Sciences (IIE, CAS), supervised by Prof. Qingming Huang (黄庆明) (IEEE Fellow). I am also lucky to have opportunities to collaborate with Qianqian Xu (许倩倩) (Professor at Institute of Computing Technology (ICT), CAS), Xiaochun Cao (操晓春) (Dean of School of Cyber Science and Technology, Sun Yat-sen University), Zhiyong Yang (杨智勇) (Tenure-track Assistant Professor at UCAS).
My research interests primarily lie in machine learning and AI safety, with a particular focus on:
- Theory of Learning to Rank and its Derived Algorithms
- AUC-oriented Learning and its Applications (e.g., downstream computer vision tasks)
- Safe AI Models and Algorithms (e.g., certified/adversarial robustness, robust and fair generative models)
I am always open to academic collaboration—please feel free to contact me if you are interested.
包世龙 (邮箱：baoshilong@ucas.ac.cn)，现为中国科学院大学博士后、特别研究助理 (合作导师：黄庆明教授)，主要研究方向为机器学习基础理论与人工智能安全，尤其聚焦鲁棒机器学习、排序学习理论与优化、无害多模态生成及大模型安全等方法与理论研究, 已在 CCF-A 类期刊/会议发表论文20余篇 (一作论文7篇)，其中TPAMI 9 篇 (IF:18.6，一作4篇)、NeurIPS/ICML 11篇等，并有多篇入选Oral/Spotlight论文。先后入选中国博士后科学基金国家资助博士后研究人员计划、北京市“高创计划”青年人才托举工程、中国科学院特别研究助理项目等人才支持计划；获 ACM China 优博奖提名（全国共5人）、ACM China SigMM 优博奖（共3人）、北京图象图形学学会优秀博士学位论文奖（京津冀共5篇）、中国科学院优秀博士学位论文奖（全学科 100 篇）、中国科学院信息工程研究所所长特别奖等荣誉奖励，并斩获多项 CCF-A 类会议国际竞赛冠军。项目承担方面，先后主持国家自然科学基金青年项目（C类）、中国博士后科学基金面上及国家资助博士后计划等多项国家级课题，并参与中科院先导 B、国家自然科学基金专项等重大项目。
欢迎对鲁棒机器学习、大模型安全、推荐系统等方向感兴趣的同学联系科研合作与实习机会。
🔥 News
- 2025.11.15-: One paper has been accepted by T-PAMI 2025!
- 2025.10.12: 🎉🎉 My PhD Thesis “Toward Efficient and Generalizable Collaborative Metric Learning Algorithms” (in Chinese) has been selected as the ACM China Excellent Doctoral Dissertation Normination Award (totally 5 papers in China) (ACM China 优博奖提名)
- 2025.09.20: One paper has been accepted by NeurIPS 2025!
- 2025.09.15: One paper has been accepted by T-PAMI!
- 2025.09.13: We are organizing the forum “Efficient Training and Inference of Large Models” at the CSIG Young Scientists Conference 2025. Welcome to join us!
- 2025.08.01: Our team won the 1st Place Award in ICCV 2025 Competition for High-Quality Face Dataset Generation (DataCV Challenge), with one paper accepted by ICCV 2025 workshop!
- 2025.06.30: 🎉🎉 My PhD Thesis “Toward Efficient and Generalizable Collaborative Metric Learning Algorithms” (in Chinese) has been selected as the Distinguished Dissertation Award of Chinese Academy of Sciences (totally 100 papers) (中国科学院百篇优博论文)
- 2025.06.18: 🎉🎉 Our team (MR-CAS) won the 1st Place Award in CVPR 2025 Workshop on Compositional 3D Vision (C3DV 3DCoMPaT-200, Coarse-Grained GCR Track)
- 2025.06.12: 🎉🎉 Our team (MR-CAS) won the 1st Place Award in CVPR 2025 Competition for Fine-grained Video Understanding (EgoVis HoloAssist Challenges: Mistake Detection Track).
- 2025.05.20: 🎉🎉 I have been nominated as ICLR Notable Reviewer 2025.
- 2025.05.02: 🎉🎉 Three papers have been accepted by ICML 2025.
- 2025.02.20: 🎉🎉 One papers have been accepted by T-PAMI 2025.
✨ Highlight Paper
Towards Size-invariant Salient Object Detection: A Generic Evaluation and Optimization Approach.
Shilong Bao, Qianqian Xu, Feiran Li, Boyu Han, Zhiyong Yang, Xiaochun Cao, and Qingming Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2025.
AUCPro: AUC-Oriented Provable Robustness Learning.
Shilong Bao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, and Qingming Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 47(6): 4579-4596, Jun. 2025. |[Code]|
Improved Diversity-Promoting Collaborative Metric Learning for Recommendation.
Shilong Bao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, and Qingming Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 46(12): 9004-9022, Jun. 2024. |[Code]|
Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling.
Shilong Bao, Qianqian Xu, Zhiyong Yang, Xiaochun Cao and Qingming Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 45(1): 1017-1035, Jan. 2023. |[Code]|
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm.
Shilong Bao, Qianqian Xu, Zhiyong Yang , Yuan He, Xiaochun Cao, Qingming Huang. Advances in Neural Information Processing Systems (NeurIPS), 2451-2464, 2022. (Oral, 1.7%) | [Code]| [Video] | [Poster] | [Slides]
![[Poster]](https://nips.cc/media/PosterPDFs/NeurIPS%202022/55412.png?t=1668477709.8846257){kind=link}
When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC.
Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao and Qingming Huang. International Conference on Machine Learning (ICML), 11820–11829, 2021. (Long Talk, 3%) | [Code]| [Video] | [Poster] | [Slides]
![[Poster]](https://github.com/statusrank/A-Generic-Framework-for-Optimizing-Two-way-Partial-AUC/blob/main/TPAUC_poster.png){kind=link}
📝 Publications
2025
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Closing the Approximation Gap of Partial AUC Optimization: A Tale of Two Formulations. Yangbangyan Jiang, Qianqian Xu, Huiyang Shao, Zhiyong Yang, Shilong Bao, Xiaochun Cao and Qingming Huang.IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2025. |[Code]|
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LightFair: Towards an Efficient Alternative for Fair T2I Diffusion via Debiasing Pre-trained Text Encoders. Boyu Han, Qianqian Xu,Shilong Bao, Zhiyong Yang, Kangli Zi, and Qingming Huang. Annual Conference on Neural Information Processing Systems (NeurIPS), 2025. |[Code]|
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One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing Framework for Diffusion Models. Feiran Li, Qianqian Xu, Shilong Bao, Zhiyong Yang, Xiaochun Cao, Qingming Huang. International Conference on Machine Learning (ICML), 2025. |[Code]|
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MixBridge: Heterogeneous Image-to-Image Backdoor Attack through Mixture of Schrödinger Bridges. Shixi Qin, Zhiyong Yang, Shilong Bao, Shi Wang, Qianqian Xu, Qingming Huang. International Conference on Machine Learning (ICML), 2025. |[Code]|
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OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning. Cong Hua, Qianqian Xu, Zhiyong Yang, Zitai Wang, Shilong Bao, Qingming Huang. International Conference on Machine Learning (ICML), 2025. |[Code]|
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Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification. Zhiguang Lu, Qianqian Xu, Shilong Bao, and Zhiyong Yang and Qingming Huang. AAAI Conference on Artificial Intelligence (AAAI), 2025. |[Code]|
2024
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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation. Boyu Han, Qianqian Xu, Zhiyong Yang, Shilong Bao, Peisong Wen, Yangbangyan Jiang and Qingming Huang. Advances in Neural Information Processing Systems (NeurIPS), 2024. |[Code]|
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ReconBoost: Boosting Can Achieve Modality Reconcilement. Cong Hua, Qianqian Xu, Shilong Bao, Zhiyong Yang and Qingming Huang. International Conference on Machine Learning (ICML), 2024 | [Code] |
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Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition. Zhiyong Yang, Qianqian Xu, Zitai Wang, Sicong Li, Boyu Han, Shilong Bao, Xiaochun Cao and Qingming Huang. International Conference on Machine Learning (ICML), 2024 | [Code] |
Earlier Publications
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Revisiting AUC-oriented Adversarial Training with Loss-Agnostic Perturbations. Zhiyong Yang, Qianqian Xu, Wenzheng Hou, Shilong Bao, Yuan He, Xiaochun Cao and Qingming Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2023. | [Code] |
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AUC-Oriented Domain Adaptation: From Theory to Algorithm. Zhiyong Yang, Qianqian Xu, Shilong Bao, Peisong Wen, Yuan He, Xiaochun Cao and Qingming Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2023. | [Code] |
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Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm. Huiyang Shao, Qianqian Xu, Zhiyong Yang, Shilong Bao and Qingming Huang. Advances in Neural Information Processing Systems (NeurIPS), 38667–38679, 2022. | [Code] |
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Optimizing Two-way Partial AUC with an End-to-end Framework. Zhiyong Yang, Qianqian Xu, Shilong Bao, Yuan He, Xiaochun Cao and Qingming Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 10228-10246, 2022. | [Code] |
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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems. Wenzheng Hou, Qianqian Xu, Zhiyong Yang, Shilong Bao, Yuan He and Qingming Huang. International Conference on Machine Learning (ICML), 8903–8925, 2022. | [Code] |
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Learning with Multiclass AUC: Theory and Algorithms. Zhiyong Yang, Qianqian Xu, Shilong Bao, Xiaochun Cao and Qingming Huang. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 7747–7763, 2021. | [Code] |
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Collaborative Preference Embedding against Sparse Labels. Shilong Bao, Qianqian Xu, Ke Ma, Zhiyong Yang, Xiaochun Cao and Qingming Huang. ACM International Conference on Multimedia (ACM-MM), 2079–2087, 2019. | [Code]|
📖 Services
Conferences
- ICML: PC Member (2022-2025)
- ICLR: PC Member (2024-2026)
- NeurIPS: PC Member (2023-2026)
- CVPR: PC Member (2024-2026)
- ICCV: PC Member (2025)
- WACV: PC Member (2025)
- AAAI: PC Member (2023-2026)
- AISTATS: PC Member (2025-2026)
Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI): Reviewer
- IEEE Transactions on Multimedia (T-MM): Reviewer
- IEEE Transactions on Circuits and Systems for Video Technology publication information (T-CSVT): Reviewer
- ACM Transactions on Multimedia Computing, Communications and Applications (TOMM): Reviewer
- Multimedia Systems: Reviewer
Others
- 2025.11 Program Chair of Beijing Youth Science and Technology Salon: Multimodal Intelligent Perception and Cross-modal Computing (北京青年科技沙龙)
- 2025.08 Co-chair of Efficient Training and Inference of Large Models at the CSIG Young Scientists Conference 2025
🎖 Honors and Awards
- 2025 ACM China Excellent Doctoral Dissertation Award Normination (ACM中国优博奖提名, 5 papers in China)
- 2025 ACM China SIGMM Excellent Doctoral Dissertation Award (ACM中国SigMM优博, 3 papers in total)
- 2025 Distinguished Dissertation Award of Chinese Academy of Sciences (totally 100 papers) (中国科学院优秀博士学位论文，中科院全学科100篇)
- 2025 1st Place Award in ICCV 2025 Competition for High-Quality Face Dataset Generation (DataCV Challenge)
- 2025 1st Place Award at the 3rd CVPR Workshop on Compositional 3D Vision (Coarse-Grained GCR Track Challenge, 2025)
- 2025 1st Place Award in CVPR EgoVis HoloAssist Challenges for Fine-grained Video Understanding (Mistake Detection Track, 2025)
- 2025 ICLR Notable Reviewer (480/all)
- 2025 Young Elite Scientists Sponsorship Program of the Beijing High Innovation Plan (北京”高创计划”-青年人才托举工程)
- 2024 Outstanding Doctoral Dissertation Award of Beijing Society of Image and Graphics (BSIG). (北京图象图形学学会优秀博士学位论文奖 ( 京津冀5篇 ))
- 2023 Zhuliyuehua Scholarship for Excellent Doctoral Student, CAS. (中国科学院朱李月华奖学金，中科院共300人)
- 2022 National Scholarship, Ministry of Education of the People’s Republic of China. (国家奖学金)
- 2021 Director Special Scholarship Award, IIE, CAS. (中科院信息工程研究所所长特别奖)
- 2017 The ACM-ICPC Asia Regional Contest Qingdao Site 2017 Silver Medal (ACM-ICPC 亚洲区域赛 (青岛站))
- 2017 The ACM-ICPC Asia Regional Contest Xian Site 2017 Bronze Medal (ACM-ICPC 亚洲区域赛 (西安站))
- 2017 3rd China Collegiate Programming Contest Harbin Site Bronze Medal (第三届中国大学生程序设计竞赛 CCPC (哈尔滨站))
🎓 Educations
2019.09 - 2024.06, Ph.D. in Computer Applied Technology.
Institute of Information Engineering, Chinese Academy of Sciences (IIE, CAS).
University of Chinese Academy of Sciences, Beijing.
2015.09 - 2019.06, Undergraduate.
College of Computer Science and Technology.
Qingdao University (QDU), Qingdao.
💬 Invited Talks
2025.11: Towards Harmless Multimodal Generation: Challenges and Preliminary Pathways . [Website] | [Video]
Abstract: Generative AI is reshaping digital creation, but its potential for harmful generation remains a bottleneck to real-world deployment. This talk reports our early efforts toward harmless generation along three strands: (i) avoiding harmful content via targeted model unlearning; (ii) mitigating generation bias with lightweight fair interventions; and (iii) exposing backdoor vulnerabilities to inform robust defenses. Across empirical studies, these directions preserve utility while showing encouraging effectiveness, pointing to promising avenues for future work.
2025.08: Efficient and Generalizable Robust Collaborative Ranking. [Website]
Abstract: Collaborative Ranking is a fundamental technique in tasks such as representation learning, content retrieval, and multimedia recommendation. However, it often faces significant challenges when dealing with large-scale web data, including limited representational capacity, low computational efficiency, and poor robustness, hindering the generalization ability of models. While existing research primarily focuses on model architecture and loss function optimization, systematic theoretical analysis of generalization remains limited. This report introduces a generalization theory framework for collaborative ranking, which further guides the design and optimization of ranking algorithms toward developing efficient, robust, and generalizable solutions.
- 2023.02: AI TIME Youth PhD Talk of NeurIPS2022. [Video].
- 2022.11: Oral presentation at NeurIPS conference [Video].
💻 Fundings and Project
- 2025.08: Young Scientists Fund of the National Natural Science Foundation of China (NSFC青年基金C类, No.62502496, PI )
- 2025.07: General Program of the Chinese Postdoctoral Science Foundation (中国博士后科学基金面上资助, No.2025M771492, PI )
- 2025.06: CAS Special Research Assistant Talent Support Program (中国科学院特别研究助理资助项目, PI )
- 2025.07: Beijing Youth Science and Technology Salon (北京青年科技沙龙项目, PI )
- 2025.01: National Natural Science Foundation of China (NSFC), Special Project (NSFC专项项目, No. 62441232, Core Member )
- 2024.07: Postdoctoral Fellowship Program of the Chinese Postdoctoral Science Foundation (中国博士后科学基金会国家资助博士后研究人员计划（B档）, No.GZB20240729, PI )
2020.02 - now: As a core member, I participated in the development of XCurve: Machine Learning with Decision-Invariant Metrics.
- Machine learning and deep learning technologies have recently been successfully employed in many complicated high-stake decision-making applications. The goal of Xcurve learning is to learn high-quality models that can adapt to different decision conditions, which provides a systematic solution to optimize the area under different kinds of performance curves. Welcome to try now and give us feedback!