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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, CAIR-HKBU fellowship, and multiple government & industry-funded fellowship.

• Nov 2022: TMLR Group is honored to jointly receive NeurIPS'22 Outstanding Paper Award.

• Aug 2022: TMLR Group will jointly organize Online Asian Machine Learning School 2022.

• Aug 2022: I am honored to join the Editorial Board of JMLR and MLJ.

• Aug 2022: I am honored to 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 receive Tencent AI Faculty Research Award 2022.

• 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. Sugiyama.

• Feb 2022: TMLR Group is honored to receive Alibaba Faculty Research Award 2022.

• Jan 2022: RIKEN Team will organize TrustML Young Scientist Seminars 2022.

• Dec 2021: I am honored to serve as Area Chair for NeurIPS, ICML and ICLR (see Professional Service).

• Oct 2021: TMLR Group is honored to receive MSRA 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.

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Research

My research interests lie in machine learning and deep learning. My long-term goal is to develop trustworthy intelligent systems, which can learn and reason from a massive volume of complex (e.g., weakly supervised, self-supervised, robust, causal, fair, and privacy-preserving) data (e.g, label, example, preference, domain, similarity, graph, and demonstration) automatically and federatively. Recently, I develop core machine learning methodology. Besides, I am actively applying our fundamental research into the interdisciplinary domain. I am heading Trustworthy Machine Learning and Reasoning Group.

My current research work center around four major themes (with representative works/projects; more information here):

• Weakly Supervised and Self-supervised Representation Learning: [Survey] [NeurIPS'18] [ICML'19] [ICML'20] [ICML'21] [ICML'22]

• Robustness, Security and Privacy in Machine Learning: [ICML'20] [ICML'21] [ICLR'21] [NeurIPS'22] [ICLR'22]

• Federated, Automated and Graph Machine Learning: [ICML'20] [KDD'21] [ICML'22] [ICLR'22]

• Interdisciplinary Problems (e.g., Healthcare Analytics and Drug Discovery): [Project'20] [NeurIPS'22]

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Selected Projects

• RGC ECS (PI): Trustworthy Deep Learning from Open-set Corrupted Data [Link] [Website]

• NSFC YSF (PI): The Research on the Automated Trustworthy Machine Learning

• GDST BRF (PI): Trustworthy Deep Reasoning with Human-level Constraints

• RIKEN CRF (PI): New Directions in Trustworthy Machine Learning

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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)

• 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)

• 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].

• 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].

• 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].

• 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].

• 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].

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Brief Biography

Bo Han is currently an Assistant Professor of Computer Science and a Director of Trustworthy Machine Learning and Reasoning Group at Hong Kong Baptist University, and a BAIHO Visiting Scientist at RIKEN Center for Advanced Intelligence Project (RIKEN AIP). He was a Visiting Faculty Researcher at Microsoft Research (2022) and a Postdoc Fellow at RIKEN AIP (2019-2020). He received his Ph.D. degree in Computer Science from University of Technology Sydney (2015-2019). 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). He has co-authored a machine learning monograph, including Machine Learning with Noisy Labels (MIT Press). He has served as area chairs of NeurIPS, ICML and ICLR, senior program committees of KDD, AAAI and IJCAI, and program committees of AISTATS, UAI and CLeaR. He has also served as action (associate) editors of Transactions on Machine Learning Research, Neural Networks and IEEE Transactions on Neural Networks and Learning Systems, and editorial board members of Journal of Machine Learning Research and Machine Learning Journal. He received Outstanding Paper Award at NeurIPS and Outstanding Area Chair at ICLR. He received the RIKEN BAIHO Award (2019), RGC Early CAREER Scheme (2020), MSRA StarTrack Program (2021), and Tencent AI Faculty Research Award (2022).

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Acknowledgement

TMLR group is/was gratefully supported by UGC and RGC of Hong Kong (Awards ECS22200720, RMGS20221102, RMGS20221306), NSF of China (Award YSF62006202) and Guangdong (Award BRF2022A1515011652), RIKEN AIP, HKBU, HKBU CSD, CAAI, and industry research labs (Microsoft, Google, Nvidia, Tencent, Alibaba).