Jianing Zhu

Incoming Postdoc → UT Austin

PhD @ TMLR Group
Department of Computer Science
Faculty of Science, Hong Kong Baptist University
Kowloon Tong, Hong Kong SAR

[Google Scholar] [Github] [Twitter] [LinkedIn]

E-mail: csjnzhu@comp.hkbu.edu.hk or zhujianing9810@gmail.com

Address: DLB 625, Level 6, David C. Lam Building, Shaw Campus,
Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR

We are actively seeking for researchers to share their research work about trustworthy machine learning on TrustML YSS, please see this page for more information and contact us if you are interested in. See [video channel] for previous seminars.
Our TMLR Group also initiated the TMLR Young Scientist Seminar for sharing diverse and cutting-edge research.

About Me

I am an incoming Postdoc at UT Austin, and got my PhD at Trustworthy Machine Learning and Reasoning (TMLR) Group in the Department of Computer Science, Hong Kong Baptist University, advised by Dr. Bo Han. I was a visiting student researcher at CMU MLD, fortunately working with Prof. Pradeep Ravikumar, and I was a research intern at RIKEN AIP, fortunately working with Dr. Gang Niu and Prof. Masashi Sugiyama. Before that, I received my B.Eng. degree of Computer Science and Technology (Top-notched Student Program) from Sichuan University in 2021.

My research interests lie in robust machine learning for building human-aligned machine intelligence, particularly in developing methodologies that improve model robustness (e.g., for adversarial examples), reliability (e.g., for out-of-distribution data), and transparency (e.g., for functionality and traceability), as well as its applications to construct responsible AI and benefit social goods. I am always open for possible collaborations. Please feel free to drop me an email if there is any suitable ideas or opportunities to discuss.

Recent Projects

(For more details can click the images)

Research Experience

Visiting PhD Student, 2025.01 - 2025.06
Statistical & Symbolic Learning (Neuro-Symbolic AI) Group, Machine Learning Department,
School of Computer Science, Carnegie Mellon University
Advised by Prof. Pradeep Ravikumar
Pittsburgh, Pennsylvania, Unite States
Research Intern, 2023.12 - 2024.05
Imperfect Information Learning Team, RIKEN AIP
Advised by Dr. Gang Niu and Prof. Masashi Sugiyama
RIKEN, Tokyo, Japan

Education

Ph.D. student, 2021.09 - 2025.07
Department of Computer Science, Faculty of Science
Hong Kong Baptist University (HKBU), Hong Kong SAR
B.Eng., 2017.09 - 2021.06
College of Computer Science (Top-notched Student Program)
Sichuan University (SCU), Chengdu, China

Selected Publication

(* indicates equal contribution, full list can refer to Google Scholar)

Conference Paper

Q. Wang, B. Han, Y. Pu, J. Zhu, T. Liu, M. Sugiyama.
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning.
In International Conference on Learning Representations (ICLR 2025), Singapore, 2025.
[PDF] [BibTeX]

@misc{wang2024unlearning,
        title={Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning}, 
        author={Qizhou Wang and Bo Han and Puning Yang and Jianing Zhu and Tongliang Liu and Masashi Sugiyama},
        year={2024},
        eprint={2406.09179},
        archivePrefix={arXiv},
}

B. Zhang*, J. Zhu*, Z. Wang, T. Liu, B. Du, B. Han.
What If the Input is Expanded in OOD Detection?
In Advances in Neural Information Processing Systems (NeurIPS 2024), Vancouver, 2024.
[PDF] [Code] [BibTeX]

@inproceedings{zhang2024what,
        title={What If the Input is Expanded in OOD Detection?},
        author={Zhang, Boxuan and Zhu, Jianing and Wang, Zengmao and Liu, Tongliang and Du, Bo and Han, Bo},
        booktitle={The Thirty-Eighth Annual Conference on Neural Information Processing Systems},
        year={2024},
}

Y. Geng, J. Zhu, J. Yao, B. Han.
Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection
In Advances in Neural Information Processing Systems (NeurIPS 2024), Vancouver, 2024.
[PDF] [Code] [BibTeX]

@inproceedings{geng2024self,
        title={Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection},
        author={Geng, Yu and Zhu, Jianing and Yao, Jiangchao and Han, Bo},
        booktitle={The Thirty-Eighth Annual Conference on Neural Information Processing Systems},
        year={2024},
}

Z. Zhou, R. Tao, J. Zhu, Y. Luo, Z. Wang, B. Han.
Benchmarking the Reasoning Robustness against Noisy Rationales in Chain-of-thought Prompting
In Advances in Neural Information Processing Systems (NeurIPS 2024), Vancouver, 2024.
[PDF] [Code] [BibTeX]

@inproceedings{zhou2024benchmarking,
        title={Benchmarking the Reasoning Robustness against Noisy Rationales in Chain-of-thought Prompting},
        author={Zhou, Zhanke and Tao, Rong and Zhu, Jianing and Luo, Yiwen and Wang, Zengmao and Han, Bo},
        booktitle={The Thirty-Eighth Annual Conference on Neural Information Processing Systems},
        year={2024},
}

J. Zhu, G. Yu, J. Yao, T. Liu, G. Niu, M. Sugiyama, B. Han.
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation.
In Advances in Neural Information Processing Systems (NeurIPS 2023), New Orleans, 2023.
[PDF] [Code] [BibTeX]

@inproceedings{
        zhu2023diversified,
        title={Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation},
        author={Jianing Zhu, Geng Yu, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han},
        booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
        year={2023}
}

J. Zhu, H. Li, J. Yao, T. Liu, J. Xu, B Han.
Unleashing Mask: Explore the Intrinsic Out-of-distribution Detection Capability.
In International Conference on Machine Learning (ICML 2023), Hawaii, 2023.
[PDF] [Code] [BibTeX]

@inproceedings{zhu2023unleashing,
        title={Unleashing Mask: Explore the Intrinsic Out-of-distribution Detection Capability}, 
        author={Jianing Zhu and Hengzhuang Li and Jiangchao Yao and Tongliang Liu and Jianliang Xu and Bo Han},
        booktitle = {International Conference on Machine Learning},
        year = {2023}
}

J. Zhu, J. Yao, T. Liu, Q. Yao, J. Xu, and B. Han.
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning.
In International Conference on Learning Representations (ICLR 2023), Kigali, 2023.
[PDF] [Code] [BibTeX]

@inproceedings{
    zhu2023combating,
    title={Combating Exacerbated Heterogeneity for Robust Models in Federated Learning},
    author={Jianing Zhu and Jiangchao Yao and Tongliang Liu and Quanming Yao and Jianliang Xu and Bo Han},
    booktitle={The Eleventh International Conference on Learning Representations },
    year={2023},
    url={https://openreview.net/forum?id=eKllxpLOOm}
}

J. Zhou*, J. Zhu*, J. Zhang, T. Liu, G. Niu, B. Han, M. Sugiyama.
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks.
In Advances in Neural Information Processing Systems (NeurIPS 2022), Online, 2022.
[PDF] [Code] [BibTeX] [Spotlight]

@inproceedings{
    zhou2022adversarial,
    title={Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks},
    author={Jianan Zhou and Jianing Zhu and Jingfeng Zhang and Tongliang Liu and Gang Niu and Bo Han and Masashi Sugiyama},
    booktitle={Advances in Neural Information Processing Systems},
    editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
    year={2022},
    url={https://openreview.net/forum?id=s7SukMH7ie9}
}

J. Zhu, J. Yao, B. Han, J. Zhang, T. Liu, G. Niu, J. Zhou, J. Xu, H. Yang.
Reliable Adversarial Distillation with Unreliable Teachers.
In International Conference on Learning Representations (ICLR 2022), Online, 2022.
[PDF] [Code] [BibTeX]

@inproceedings{
    zhu2022reliable,
    title={Reliable Adversarial Distillation with Unreliable Teachers},
    author={Jianing Zhu and Jiangchao Yao and Bo Han and Jingfeng Zhang and Tongliang Liu and Gang Niu and Jingren Zhou and Jianliang Xu and Hongxia Yang},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=u6TRGdzhfip}
}

J. Zhang, J. Zhu, G. Niu, B. Han, M. Sugiyama, and M. Kankanhalli.
Geometry-aware Instance-reweighted Adversarial Training.
In International Conference on Learning Representations (ICLR 2021), Online, 2021.
[PDF] [Code] [BibTeX] [Oral]

@inproceedings{
    zhang2021geometryaware,
    title={Geometry-aware Instance-reweighted Adversarial Training},
    author={Jingfeng Zhang and Jianing Zhu and Gang Niu and Bo Han and Masashi Sugiyama and Mohan Kankanhalli},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=iAX0l6Cz8ub}
}

Working Preprint

J. Zhu, B. Han, J. Yao, J. Xu, G. Niu, M. Sugiyama.
Decoupling the Class Label and the Target Concept in Machine Unlearning.
In arXiv, 2024.
[PDF] [BibTeX]

@misc{zhu2024decoupling,
        title={Decoupling the Class Label and the Target Concept in Machine Unlearning}, 
        author={Jianing Zhu and Bo Han and Jiangchao Yao and Jianliang Xu and Gang Niu and Masashi Sugiyama},
        year={2024},
        eprint={2406.08288},
        archivePrefix={arXiv},
}

Honors & Award

NeurIPS Top Reviewers, NeurIPS (2023)
ICML Best Reviewers (Top 10%), ICML (2021,2024)
ICLR Notebale Reviewers, ICLR (2025)
Yakun Scholarship Scheme for Mainland Postgraduate Students, HKBU (2024)
Computer Science Department Research Excellence Award, HKBU (2023)
Computer Science Department RPg Performance Award, HKBU (2022, 2023, 2024)
Excellent Teaching Assistant Performance Award, HKBU (2022, 2023)
Nominated for Hong Kong PhD Fellowship Scheme (HKPFS), HKBU (2021)
National Scholarship, Ministry of Education (2018)
University Scholarship, SCU (2018, 2019, 2020)

Academic Talk

Invited Talk about Trustworthy Machine Learning @ SCU Top-notched Program, Online, Dec.09, 2023.
Youth PhD Talk for NeurIPS 2023 @ AI Time, Online, Nov.02, 2023. [Video]
Invited Talk about SFAT @ XMU ASC, Online, Aug.14, 2023.
Youth PhD Talk for ICLR 2023 @ AI Time, Online, Jun.06, 2023. [Video]

Academic Service

Organizing Committee

Workshop Chair Assistant, NeurIPS 2024.
Workshop Assistant, HKBU-COMP & RIKEN-AIP Joint Workshop on AI and ML 2024.
Executive Group Member, RIKEN TrustML Young Scientist Seminars.
Organizer, TMLR Young Scientist Seminars.
Conference Reviewer \ PC Member:

ICML [Top Reviewer], NeurIPS [Top Reviewer], ICLR [Notebale Reviewer, Area Chair in Tiny Paper Track], AISTATS, KDD, ACML, AAAI, IJCAI.
Journal Reviewer:

ACM Computing Surveys, TPAMI, TMLR, IJCV, JAIR, TNNLS, MLJ, NN, PR.

Teaching

(Assistance in following courses)

COMP7250(PG): Machine Learning, Spring (2022)
COMP7240(PG): Recommender Systems, Autumn (2022, 2023)
COMP7160(PG): Research Methods in Computer Science, Autumn (2022,2023)
COMP4135(UG): Recommender Systems and Applications, Autumn (2022, 2023)
COMP3057(UG): Intro to AI and Machine Learning, Autumn (2022)