Jianing ZhuPh.D. Student
TMLR Group
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[Github]
[LinkedIn]
E-mail: csjnzhu [at] comp.hkbu.edu.hk |
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I am currently a third-year Ph.D. student at Trustworthy Machine Learning and Reasoning (TMLR) Group in the Department of Computer Science, Hong Kong Baptist University, advised by Dr. Bo Han. 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 trustworthy machine learning for building human-aligned machine intelligence, particularly in developing methodologies that improve its robustness (for adversarial examples), reliability (for out-of-distribution data), and alignment (with human value), as well as its applications to enhance AI safety 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.
Research Intern, 2023.12 - present Imperfect Information Learning Team, RIKEN AIP Advised by Dr. Gang Niu and Prof. Masashi Sugiyama RIKEN, Tokyo, Japan
Ph.D. student, 2021.09 - 2025.08 (expected) 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
@misc{li2023deepinception, title={DeepInception: Hypnotize Large Language Model to Be Jailbreaker}, author={Xuan Li and Zhanke Zhou and Jianing Zhu and Jiangchao Yao and Tongliang Liu and Bo Han}, year={2023}, eprint={2311.03191}, archivePrefix={arXiv}, primaryClass={cs.LG} }
@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} }
@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} }
@inproceedings{zhu2023exploring, title={Exploring Model Dynamics for Accumulative Poisoning Discovery}, author={Jianing Zhu and Xiawei Guo and Jiangchao Yao and Chao Du and Li He and Shuai Yuan and Tongliang Liu and Liang Wang and Bo Han}, booktitle = {International Conference on Machine Learning}, year = {2023} }
@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} }
@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} }
@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} }
@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} }
COMP7240(PG): Recommender Systems, Autumn (2022, 2023)
COMP7160(PG): Research Methods in Computer Science, Autumn (2022,2023)
COMP7250(PG): Machine Learning, Spring (2022)
COMP4135(UG): Recommender Systems and Applications, Autumn (2022, 2023)
COMP3057(UG): Intro to AI and Machine Learning, Autumn (2022)