Bo Han
Home
News
TMLR Group is always looking for highly self-motivated PhD/RA/Visiting students and Postdoc researchers to work on trustworthy learning and reasoning algorithms, theories and systems. Various types of fellowship are available for outstanding applicants, such as HKPFS Fellowship, NVIDIA-HKBU Fellowship, CAIR-HKBU Fellowship, and multiple government & industry-funded fellowships. TMLR Group is jointly looking for visiting scholars for Asian Trustworthy Machine Learning (ATML) Fellowships. TMLR Group releases several papers that represent our research flavors: [Co-teaching], [OOD Theory], [Watermarking], [SFAT], [GRA]. Oct 2023: TMLR Group will jointly organize HKBU COMP-RIKEN AIP Joint Workshop on Artificial Intelligence and Machine Learning. Sep 2023: I am honored to receive IEEE TNNLS Outstanding Associate Editor. Sep 2023: TMLR Group is honored to receive CCF-Baidu Faculty Research Award 2023 (from Baidu Research) [News]. Sep 2023: TMLR Group is honored to host Distinguished Lecture Series provided by Prof. Chengqi Zhang [Video]. Aug 2023: TMLR Group is honored to receive NSFC General Program 2023 [News]. Aug 2023: TMLR Group will co-author a ML monograph accepted by Springer Nature. Aug 2023: TMLR Group's collaborative work with Alibaba Research has been deployed into its online advertisement platform. Aug 2023: I will serve as an Action Editor for MLJ and an Area Chair for AISTATS'24. Jul 2023: TMLR Group will co-author an interdisciplinary article published by Nature Plants. Jul 2023: I will serve as an Action Editor for JAIR. May 2023: TMLR Group is honored to host Distinguished Seminar Series provided by Prof. Kun Zhang. May 2023: TMLR Group is honored to host Distinguished Lecture Series provided by Prof. Kilian Weinberger [Video]. Mar 2023: TMLR Group is honored to host Distinguished Lecture Series provided by Prof. Tuomas Sandholm [Video]. Feb 2023: I will serve as an Area Chair for NeurIPS'23. Jan 2023: TMLR Group's collaborative work with Tencent Research achieves state-of-the-art results in GOOD benchmark. Dec 2022: I will serve as Publication & Publicity Co-Chair for ACML'23. Dec 2022: I will serve as an Area Chair for ICML'23 and UAI'23. Nov 2022: TMLR Group is honored to jointly receive NeurIPS'22 Outstanding Paper Award [Paper]. Aug 2022: TMLR Group will jointly organize Online Asian Machine Learning School 2022. Aug 2022: I will serve as an Area Chair for ICLR'23. Aug 2022: I will join the Editorial Board of JMLR and MLJ. Aug 2022: I will serve as an Action Editor for TMLR and an Associate Editor for IEEE TNNLS. Jul 2022: I am honored to receive UAI'22 Top Reviewer. May 2022: TMLR Group is honored to jointly receive NeurIPS'18 Most Influential Paper Award [Paper]. May 2022: TMLR Group is honored to receive Tencent AI Faculty Research Award 2022 (from Tencent Research) [News]. Feb 2022: RIKEN Team will author a ML monograph accepted by MIT Press. Feb 2022: TMLR Group is honored to host Distinguished Lecture Series provided by Prof. Masashi Sugiyama [Video]. Feb 2022: TMLR Group is honored to receive Alibaba Faculty Research Award 2022 (from Alibaba Research). Jan 2022: RIKEN Team will organize TrustML Young Scientist Seminars 2022. Dec 2021: I regularly serve as Area Chair for NeurIPS, ICML and ICLR (see Professional Service). Oct 2021: TMLR Group is honored to receive Microsoft Research StarTrack Program 2021 (with AI for Science team). May 2021: I am honored to receive ICLR'21 Outstanding Area Chair. Sep 2020: I am honored to receive ICML'20 Top Reviewer. Jun 2020: TMLR Group is honored to receive RGC Early CAREER Scheme 2020. Mar 2020: I am honored to receive RIKEN BAIHO Award 2019.
See more news here.
Research
My research interests lie in machine learning, deep learning and foundation models. My long-term goal is to develop trustworthy intelligent systems (e.g., trustworthy foundation models), which can learn and reason from a massive volume of complex (e.g., weakly supervised, self-supervised, out-of-distribution, causal, fair, and privacy-preserving) data (e.g, label, example, preference, domain, similarity, graph, and demonstration) federatively and automatically. Recently, I develop core machine learning methodology. Besides, I am actively applying our fundamental research into the interdisciplinary domain.
Selected Projects
NSFC General Program (PI, Machine Learning): The Research on Trustworthy Federated Learning in Imperfect Environments [Link] [Website] RGC Early CAREER Scheme (PI, Artificial Intelligence and Machine Learning): Trustworthy Deep Learning from Open-set Corrupted Data [Link] [Website] NSFC Young Scientists Fund (PI, Machine Learning): The Research on the Automated Trustworthy Machine Learning [Link] [Website] GDST Basic Research Fund (PI, Data Mining and Machine Learning): Trustworthy Deep Reasoning with Human-level Constraints RIKEN Collaborative Research Fund (PI, Machine Learning): New Directions in Trustworthy Machine Learning
Research Highlights
(* indicates advisees/co-advisees; updating through the time; see the full list here)
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning.
J. Zhu*, J. Yao, T. Liu, Q. Yao, J. Xu, and B. Han.
In Proceedings of 11th International Conference on Learning Representations (ICLR'23), [PDF] [Code] [Poster]. Latent Class-Conditional Noise Model.
J. Yao, B. Han, Z. Zhou, Y. Zhang, and I.W. Tsang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2023, [PDF] [Code]. Watermarking for Out-of-distribution Detection.
Q. Wang*, F. Liu, Y. Zhang, J. Zhang, C. Gong, T. Liu, and B. Han.
In Advances in Neural Information Processing Systems 35 (NeurIPS'22), [PDF] [Code] [Poster] [Spotlight]. Is Out-of-distribution Detection Learnable?
Z. Fang, S. Li, J. Lu, J. Dong, B. Han, and F. Liu.
In Advances in Neural Information Processing Systems 35 (NeurIPS'22), [PDF] [Poster] [Oral, Outstanding Paper Award]. CausalAdv: Adversarial Robustness through the Lens of Causality.
Y. Zhang*, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang.
In Proceedings of 10th International Conference on Learning Representations (ICLR'22), [PDF] [Code] [Poster]. Confidence Scores Make Instance-dependent Label-noise Learning Possible.
A. Berthon*, B. Han, G. Niu, T. Liu, and M. Sugiyama.
In Proceedings of 38th International Conference on Machine Learning (ICML'21), [PDF] [Code] [Poster] [Long Oral]. How does Disagreement Help Generalization against Label Corruption?
X. Yu*, B. Han, J. Yao, G. Niu, I.W. Tsang, and M. Sugiyama.
In Proceedings of 36th International Conference on Machine Learning (ICML'19), [PDF] [Code] [Poster] [Long Oral]. Towards Robust ResNet: A Small Step but A Giant Leap.
J. Zhang*, B. Han, L. Wynter, B. Low, and M. Kankanhalli.
In Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI'19), [PDF] [Code] [Poster]. Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I.W. Tsang, and M. Sugiyama.
In Advances in Neural Information Processing Systems 31 (NeurIPS'18), [PDF] [Code] [Poster] [Most Influential Paper Award]. Masking: A New Perspective of Noisy Supervision.
B. Han, J. Yao, G. Niu, M. Zhou, I.W. Tsang, Y. Zhang, and M. Sugiyama.
In Advances in Neural Information Processing Systems 31 (NeurIPS'18), [PDF] [Code] [Poster].
Selected Publications
(* indicates advisees/co-advisees; see the full list here)
Machine Learning with Noisy Labels: From Theory to Heuristics.
M. Sugiyama, N. Lu, B. Han, T. Liu, and G. Niu.
Adaptive Computation and Machine Learning series, The MIT Press, 2024, [PDF].
(the monograph is accepted; coming in 2024) Trustworthy Machine Learning under Imperfect Data.
B. Han and T. Liu.
Computer Science series, Springer Nature, 2024, [PDF].
(the monograph is accepted; coming in 2024) A Survey of Label-noise Representation Learning: Past, Present and Future.
B. Han, Q. Yao, T. Liu, G. Niu, I.W. Tsang, J.T. Kwok, and M. Sugiyama.
arXiv preprint arXiv:2011.04406, 2020, [PDF].
(the draft is kept updating; any comments and suggestions are welcome) Learning to Augment Distributions for Out-of-distribution Detection.
Q. Wang*, Z. Fang, Y. Zhang, F. Liu, Y. Li, and B. Han.
In Advances in Neural Information Processing Systems 36 (NeurIPS'23), [PDF] [Code] [Poster]. FedFed: Feature Distillation against Data Heterogeneity in Federated Learning.
Z. Yang*, Y. Zhang, Y. Zheng, X. Tian, H. Peng, T. Liu, and B. Han.
In Advances in Neural Information Processing Systems 36 (NeurIPS'23), [PDF] [Code] [Poster]. On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation.
Z. Zhou*, C. Zhou*, X. Li*, J. Yao, Q. Yao, and B. Han.
In Proceedings of 40th International Conference on Machine Learning (ICML'23), [PDF] [Code] [Poster]. Detecting Out-of-distribution Data through In-distribution Class Prior.
X. Jiang*, F. Liu, Z. Fang, H. Chen, T. Liu, F. Zheng, and B. Han.
In Proceedings of 40th International Conference on Machine Learning (ICML'23), [PDF] [Code] [Poster]. Combating Exacerbated Heterogeneity for Robust Models in Federated Learning.
J. Zhu*, J. Yao, T. Liu, Q. Yao, J. Xu, and B. Han.
In Proceedings of 11th International Conference on Learning Representations (ICLR'23), [PDF] [Code] [Poster]. A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond.
Y. Lin*, R. Pi*, W. Zhang, X. Xia, J. Gao, X. Zhou, T. Liu, and B. Han.
In Proceedings of 11th International Conference on Learning Representations (ICLR'23), [PDF] [Code] [Poster] [Spotlight]. AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning.
Y. Zhang, Z. Zhou*, Q. Yao, X. Chu, and B. Han.
In Proceedings of 29th ACM Conference on Knowledge Discovery and Data Mining (KDD'23), [PDF] [Code] [Poster]. Learning from Noisy Pairwise Similarity and Unlabeled Data.
S. Wu, T. Liu, B. Han, J. Yun, G. Niu, and M. Sugiyama.
Journal of Machine Learning Research (JMLR), 2023, [PDF] [Code]. Latent Class-Conditional Noise Model.
J. Yao, B. Han, Z. Zhou, Y. Zhang, and I.W. Tsang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2023, [PDF] [Code]. KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation.
C. Zhou*, F. Liu, C. Gong, R. Zeng, T. Liu, W.K. Cheung, and B. Han.
Transactions on Machine Learning Research (TMLR), 2023, [PDF] [Code]. Server-Client Collaborative Distillation for Federated Reinforcement Learning.
W. Mai*, J. Yao, C. Gong, Y. Zhang, Y.M. Cheung, and B. Han.
ACM Transactions on Knowledge Discovery from Data (TKDD), 2023, [PDF] [Code]. Plantorganelle Hunter is An Effective Deep-learning-based Method for Plant Organelle Phenotyping in Electron Microscopy.
X. Feng, Z. Yu, ..., F. Liu, B. Han, B. Zechmann, Y. He, and F. Liu.
Nature Plants, 2023, [PDF] [Code]. Watermarking for Out-of-distribution Detection.
Q. Wang*, F. Liu, Y. Zhang, J. Zhang, C. Gong, T. Liu, and B. Han.
In Advances in Neural Information Processing Systems 35 (NeurIPS'22), [PDF] [Code] [Poster] [Spotlight]. Is Out-of-distribution Detection Learnable?
Z. Fang, S. Li, J. Lu, J. Dong, B. Han, and F. Liu.
In Advances in Neural Information Processing Systems 35 (NeurIPS'22), [PDF] [Poster] [Oral, Outstanding Paper Award]. Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning.
Z. Tang*, Y. Zhang, S. Shi, X. He, B. Han, and X. Chu.
In Proceedings of 39th International Conference on Machine Learning (ICML'22), [PDF] [Code] [Poster]. Contrastive Learning with Boosted Memorization.
Z. Zhou*, J. Yao, Y. Wang, B. Han, and Y. Zhang.
In Proceedings of 39th International Conference on Machine Learning (ICML'22), [PDF] [Code] [Poster]. Understanding and Improving Graph Injection Attack by Promoting Unnoticeability.
Y. Chen*, H. Yang, Y. Zhang, K. Ma, T. Liu, B. Han, and J. Cheng.
In Proceedings of 10th International Conference on Learning Representations (ICLR'22), [PDF] [Code] [Poster]. CausalAdv: Adversarial Robustness through the Lens of Causality.
Y. Zhang*, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang.
In Proceedings of 10th International Conference on Learning Representations (ICLR'22), [PDF] [Code] [Poster]. Robust Weight Perturbation for Adversarial Training.
C. Yu, B. Han, M. Gong, L. Shen, S. Ge, B. Du, and T. Liu.
In Proceedings of 31th International Joint Conference on Artificial Intelligence (IJCAI'22), [PDF] [Code]. Bilateral Dependency Optimization: Defending Against Model-inversion Attacks.
X. Peng*, F. Liu, J. Zhang, J. Ye, L. Lan, T. Liu, and B. Han.
In Proceedings of 28th ACM Conference on Knowledge Discovery and Data Mining (KDD'22), [PDF] [Code] [Poster]. Device-Cloud Collaborative Recommendation via Meta Controller.
J. Yao, F. Wang, X. Ding, S. Chen, B. Han, J. Zhou, and H. Yang.
In Proceedings of 28th ACM Conference on Knowledge Discovery and Data Mining (KDD'22), [PDF] [Poster]. Fair Classification with Instance-dependent Label Noise.
S. Wu, M. Gong, B. Han, Y. Liu, and T. Liu.
In Proceedings of 1st Conference on Causal Learning and Reasoning (CLeaR'22), [PDF] [Code]. Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization.
Q. Yao, Y. Wang, B. Han, and J.T. Kwok.
Journal of Machine Learning Research (JMLR), 2022, [PDF] [Code]. Learning with Mixed Open-set and Closed-set Noisy Labels.
X. Xia, B. Han, N. Wang, J. Deng, J. Li, Y. Mao, and T. Liu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2022, [PDF] [Code]. NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels.
J. Zhang, X. Xu, B. Han, T. Liu, L. Cui, G. Niu, and M. Sugiyama.
Transactions on Machine Learning Research (TMLR), 2022, [PDF] [Code]. TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation.
H. Chi*, F. Liu, W. Yang, L. Lan, T. Liu, B. Han, W.K. Cheung, and J.T. Kwok.
In Advances in Neural Information Processing Systems 34 (NeurIPS'21), [PDF] [Code] [Poster] [Spotlight]. Instance-dependent Label-noise Learning under a Structural Causal Model.
Y. Yao, T. Liu, M. Gong, B. Han, G. Niu, and K. Zhang.
In Advances in Neural Information Processing Systems 34 (NeurIPS'21), [PDF] [Code] [Poster]. Confidence Scores Make Instance-dependent Label-noise Learning Possible.
A. Berthon*, B. Han, G. Niu, T. Liu, and M. Sugiyama.
In Proceedings of 38th International Conference on Machine Learning (ICML'21), [PDF] [Code] [Poster] [Long Oral]. Maximum Mean Discrepancy is Aware of Adversarial Attacks.
R. Gao*, F. Liu, J. Zhang, B. Han, T. Liu, G. Niu, and M. Sugiyama.
In Proceedings of 38th International Conference on Machine Learning (ICML'21), [PDF] [Code] [Poster]. Geometry-aware Instance-reweighted Adversarial Training.
J. Zhang, J. Zhu*, G. Niu, B. Han, M. Sugiyama, and M. Kankanhalli.
In Proceedings of 9th International Conference on Learning Representations (ICLR'21), [PDF] [Code] [Poster] [Oral]. Robust Early-learning: Hindering the Memorization of Noisy Labels.
X. Xia, T. Liu, B. Han, C. Gong, N. Wang, Z. Ge, and Y. Chang.
In Proceedings of 9th International Conference on Learning Representations (ICLR'21), [PDF] [Code] [Poster]. Learning with Group Noise.
Q. Wang*, J. Yao, C. Gong, T. Liu, M. Gong, H. Yang, and B. Han.
In Proceedings of 35th AAAI Conference on Artificial Intelligence (AAAI'21), [PDF] [Code]. Device-Cloud Collaborative Learning for Recommendation.
J. Yao, F. Wang, K. Jia, B. Han, J. Zhou, and H. Yang.
In Proceedings of 27th ACM Conference on Knowledge Discovery and Data Mining (KDD'21), [PDF] [Poster]. Provably Consistent Partial-Label Learning.
L. Feng, J. Lv, B. Han, M. Xu, G. Niu, X. Geng, B. An, and M. Sugiyama.
In Advances in Neural Information Processing Systems 33 (NeurIPS'20), [PDF] [Code] [Poster]. Reducing Estimation Error for Transition Matrix in Label-noise Learning.
Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama.
In Advances in Neural Information Processing Systems 33 (NeurIPS'20), [PDF] [Code] [Poster]. SIGUA: Forgetting May Make Learning with Noisy Labels More Robust.
B. Han, G. Niu, X. Yu, Q. Yao, M. Xu, I.W. Tsang, and M. Sugiyama.
In Proceedings of 37th International Conference on Machine Learning (ICML'20), [PDF] [Code] [Poster]. Variational Imitation Learning from Diverse-quality Demonstrations.
V. Tangkaratt, B. Han, M. Khan, and M. Sugiyama.
In Proceedings of 37th International Conference on Machine Learning (ICML'20), [PDF] [Code] [Poster]. Attacks Which Do Not Kill Training Make Adversarial Learning Stronger.
J. Zhang*, X. Xu, B. Han, G. Niu, L. Cui, M. Sugiyama, and M. Kankanhalli.
In Proceedings of 37th International Conference on Machine Learning (ICML'20), [PDF] [Code] [Poster]. Searching to Exploit Memorization Effect in Learning from Noisy Labels.
Q. Yao, H. Yang, B. Han, G. Niu, and J.T. Kwok.
In Proceedings of 37th International Conference on Machine Learning (ICML'20), [PDF] [Code] [Poster]. A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery.
Y. Luo, B. Han, and C. Gong.
In Proceedings of 29th International Joint Conference on Artificial Intelligence (IJCAI'20), [PDF] [Code]. Are Anchor Points Really Indispensable in Label-noise Learning?
X. Xiao, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama.
In Advances in Neural Information Processing Systems 32 (NeurIPS'19), [PDF] [Code] [Poster]. How does Disagreement Help Generalization against Label Corruption?
X. Yu*, B. Han, J. Yao, G. Niu, I.W. Tsang, and M. Sugiyama.
In Proceedings of 36th International Conference on Machine Learning (ICML'19), [PDF] [Code] [Poster] [Long Oral]. Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations.
Q. Yao, J.T. Kwok, and B. Han.
In Proceedings of 36th International Conference on Machine Learning (ICML'19), [PDF] [Code] [Poster]. Towards Robust ResNet: A Small Step but A Giant Leap.
J. Zhang*, B. Han, L. Wynter, B. Low, and M. Kankanhalli.
In Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI'19), [PDF] [Code] [Poster]. Privacy-preserving Stochastic Gradual Learning.
B. Han, I.W. Tsang, X. Xiao, L. Chen, S.-F. Fung, and C. Yu.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019, [PDF]. Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I.W. Tsang, and M. Sugiyama.
In Advances in Neural Information Processing Systems 31 (NeurIPS'18), [PDF] [Code] [Poster] [Most Influential Paper Award]. Masking: A New Perspective of Noisy Supervision.
B. Han, J. Yao, G. Niu, M. Zhou, I.W. Tsang, Y. Zhang, and M. Sugiyama.
In Advances in Neural Information Processing Systems 31 (NeurIPS'18), [PDF] [Code] [Poster]. Millionaire: A Hint-guided Approach for Crowdsourcing.
B. Han, Q. Yao, Y. Pan, I.W. Tsang, X. Xiao, Q. Yang, and M. Sugiyama.
Machine Learning Journal (MLJ), 108(5): 831–858, 2018, [PDF] [Slides]. Stagewise Learning for Noisy k-ary Preferences.
Y. Pan, B. Han, and I.W. Tsang.
Machine Learning Journal (MLJ), 107(8): 1333–1361, 2018, [PDF]. Robust Plackett-Luce Model for k-ary Crowdsourced Preferences.
B. Han, Y. Pan, and I.W. Tsang.
Machine Learning Journal (MLJ), 107(4): 675–702, 2017, [PDF].
Brief Biography
Bo Han is currently an Assistant Professor in Machine Learning and a Director of Trustworthy Machine Learning and Reasoning Group at Hong Kong Baptist University, and a BAIHO Visiting Scientist of Imperfect Information Learning Team at RIKEN Center for Advanced Intelligence Project (RIKEN AIP), where his research focuses on machine learning, deep learning, foundation models, and their applications. He was a Visiting Faculty Researcher at Microsoft Research (2022) and a Postdoc Fellow at RIKEN AIP (2019-2020), working with Prof. Masashi Sugiyama. He received his Ph.D. degree in Computer Science from University of Technology Sydney (2015-2019), advised by Prof. Ivor W. Tsang. During 2018-2019, he was a Research Intern with the AI Residency Program at RIKEN AIP, working on trustworthy representation learning (e.g., Co-teaching and Masking). He also works on causal reasoning for trustworthy learning (e.g., CausalAdv and CausalNL), and trustworthy foundation models. He has co-authored two machine learning monographs, including Machine Learning with Noisy Labels (MIT Press), and Trustworthy Machine Learning under Imperfect Data (Springer Nature). He has served as Area Chairs of NeurIPS, ICML, ICLR, UAI and AISTATS. He has also served as Action Editors and Editorial Board Members of JMLR, MLJ, TMLR, JAIR and IEEE TNNLS. He received Outstanding Paper Award at NeurIPS, Outstanding Area Chair at ICLR, and Outstanding Associate Editor at IEEE TNNLS. He received the NSFC General Program, RGC Early CAREER Scheme, RIKEN BAIHO Award, Microsoft Research StarTrack Program, and Faculty Research Awards from Baidu, Alibaba and Tencent.
Acknowledgement
TMLR group is/was gratefully supported by UGC and RGC of Hong Kong, NSFC, GDST, RIKEN AIP, CCF, CAAI, GRG, HKBU, HKBU RC, HKBU CSD, and industry research labs (Microsoft, Google, NVIDIA, Baidu, Alibaba, Tencent).
|