20202024

Research activity per year

Search results

  • 2022

    Instance-Dependent Positive and Unlabeled Learning with Labeling Bias Estimation

    Gong, C., Wang, Q., Liu, T., Han, B., You, J. J., Yang, J. & Tao, D., 1 Aug 2022, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 44, 8, p. 4163-4177 15 p.

    Research output: Contribution to journalJournal articlepeer-review

    13 Citations (Scopus)
  • Is Out-of-distribution Detection Learnable?

    Fang, Z., Li, Y., Lu, J., Dong, J., Han, B. & Liu, F., 28 Nov 2022, 36th Conference on Neural Information Processing Systems (NeurIPS 2022). Neural Information Processing Systems Foundation, 15 p. (Advances in Neural Information Processing Systems; vol. 35)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    36 Citations (Scopus)
  • Learning from Noisy Pairwise Similarity and Unlabeled Data

    Wu, S., Liu, T., HAN, B., Yu, J., Niu, G. & Sugiyama, M., Nov 2022, In: Journal of Machine Learning Research. 23, 307, 34 p., 307.

    Research output: Contribution to journalJournal articlepeer-review

    Open Access
    2 Citations (Scopus)
  • Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization

    Yao, Q., Wang, Y., Han, B. & Kwok, J. T., 22 Apr 2022, In: Journal of Machine Learning Research. 23, 60 p., 136.

    Research output: Contribution to journalJournal articlepeer-review

    Open Access
  • Meta Discovery: Learning to Discover Novel Classes given Very Limited Data

    Chi, H., Liu, F., Han, B., Yang, W., Lan, L., Liu, T., Niu, G., Zhou, M. & Sugiyama, M., 25 Apr 2022, Proceedings of Tenth International Conference on Learning Representations, ICLR 2022. International Conference on Learning Representations, p. 1-20 20 p.

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
  • Modeling Adversarial Noise for Adversarial Training

    Zhou, D., Wang, N., Han, B. & Liu, T., 17 Jul 2022, Proceedings of 39th International Conference on Machine Learning (ICML 2022). Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G. & Sabato, S. (eds.). ML Research Press, p. 27353-27366 14 p. (Proceedings of Machine Learning Research; vol. 162).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    2 Citations (Scopus)
  • NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels

    Zhang, J., Xu, X., Han, B., Liu, T., Cui, L., Niu, G. & Sugiyama, M., Jun 2022, In: Transactions on Machine Learning Research. 25 p.

    Research output: Contribution to journalJournal articlepeer-review

    Open Access
  • Reliable Adversarial Distillation with Unreliable Teachers

    Zhu, J., Yao, J., Han, B., Zhang, J., Liu, T., Niu, G., Zhou, J., XU, J. & Yang, H., 25 Apr 2022, Proceedings of Tenth International Conference on Learning Representations, ICLR 2022. International Conference on Learning Representations, 15 p.

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    7 Citations (Scopus)
  • Robust Weight Perturbation for Adversarial Training

    Yu, C., Han, B., Gong, M., Shen, L., Ge, S., Bo, D. & Liu, T., 23 Jul 2022, Proceedings of the 31st International Joint Conference on Artificial Intelligence, IJCAI 2022. De Raedt, L. (ed.). International Joint Conferences on Artificial Intelligence, p. 3688-3694 7 p. (IJCAI International Joint Conference on Artificial Intelligence).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
  • Understanding and Improving Graph Injection Attack by Promoting Unnoticeability

    Chen, Y., Yang, H., Zhang, Y., Ma, K., Liu, T., Han, B. & Cheng, J., 25 Apr 2022, Proceedings of Tenth International Conference on Learning Representations, ICLR 2022. International Conference on Learning Representations, p. 1-42 42 p.

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
  • Understanding Robust Overfitting of Adversarial Training and Beyond

    Yu, C., Han, B., Shen, L., Yu, J., Gong, C., Gong, M. & Liu, T., 17 Jul 2022, Proceedings of 39th International Conference on Machine Learning (ICML 2022). Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G. & Sabato, S. (eds.). ML Research Press, p. 25595-25610 16 p. (Proceedings of Machine Learning Research; vol. 162).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    14 Citations (Scopus)
  • Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning

    Tang, Z., Zhang, Y., Shi, S., He, X., Han, B. & Chu, X., 17 Jul 2022, Proceedings of 39th International Conference on Machine Learning (ICML 2022). Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G. & Sabato, S. (eds.). ML Research Press, p. 21111-21132 22 p. (Proceedings of Machine Learning Research; vol. 162).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    17 Citations (Scopus)
  • Watermarking for Out-of-distribution Detection

    Wang, Q., Liu, F., Zhang, Y., Zhang, J., Gong, C., Liu, T. & Han, B., 28 Nov 2022, 36th Conference on Neural Information Processing Systems (NeurIPS 2022). Neural Information Processing Systems Foundation, p. 1-13 13 p. (Advances in Neural Information Processing Systems; vol. 35)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    8 Citations (Scopus)
  • 2021

    A bi-level formulation for label noise learning with spectral cluster discovery

    Luo, Y., Han, B. & Gong, C., Jan 2021, Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020. Bessiere, C. (ed.). International Joint Conferences on Artificial Intelligence, p. 2605-2611 7 p. (IJCAI International Joint Conference on Artificial Intelligence; vol. 2021-January).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    11 Citations (Scopus)
  • Confidence Scores Make Instance-dependent Label-noise Learning Possible

    Berthon, A., Han, B., Niu, G., Liu, T. & Sugiyama, M., 18 Jul 2021, Proceedings of the 38th International Conference on Machine Learning (ICML 2021). Meila, M. & Zhang, T. (eds.). ML Research Press, p. 825-836 12 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    38 Citations (Scopus)
  • Confusable Learning for Large-Class Few-Shot Classification

    Li, B., Han, B., Wang, Z., Jiang, J. & Long, G., 25 Feb 2021, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings. Hutter, F., Kersting, K., Lijffijt, J. & Valera, I. (eds.). Springer Science and Business Media Deutschland GmbH, p. 707-723 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12458 LNAI).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    1 Citation (Scopus)
  • Device-Cloud Collaborative Learning for Recommendation

    Yao, J., Wang, F., Jia, K., Han, B., Zhou, J. & Yang, H., 14 Aug 2021, KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery (ACM), p. 3865-3874 10 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    22 Citations (Scopus)
  • Fraud Detection under Multi-Sourced Extremely Noisy Annotations

    Zhang, C., Wang, Q., Liu, T., Lu, X., Hong, J., Han, B. & Gong, C., 26 Oct 2021, CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM), p. 2497-2506 10 p. (Proceedings of International Conference on Information and Knowledge Management).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    5 Citations (Scopus)
  • Geometry-aware Instance-reweighted Adversarial Training

    Zhang, J., Zhu, J., Niu, G., Han, B., Sugiyama, M. & Kankanhalli, M., May 2021, Proceedings of Ninth International Conference on Learning Representations, ICLR 2021. International Conference on Learning Representations, p. 1-29 29 p.

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
  • HyperGraph Convolution Based Attributed HyperGraph Clustering

    Fanseu Kamhoua, B., Zhang, L., Ma, K., Cheng, J. S. C., Li, B. & Han, B., 26 Oct 2021, CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. Association for Computing Machinery (ACM), p. 453-463 11 p. (Proceedings of International Conference on Information and Knowledge Management).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    5 Citations (Scopus)
  • Instance-dependent Label-noise Learning under a Structural Causal Model

    Yao, Y., Liu, T., Gong, M., Han, B., Niu, G. & Zhang, K., 6 Dec 2021, 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural Information Processing Systems Foundation, Vol. 6. p. 4409-4420 12 p. (Advances in Neural Information Processing Systems; vol. 34)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    22 Citations (Scopus)
  • Learning Diverse-Structured Networks for Adversarial Robustness

    Du, X., Zhang, J., Han, B., Liu, T., Rong, Y., Niu, G., Huang, J. & Sugiyama, M., 18 Jul 2021, Proceedings of the 38th International Conference on Machine Learning (ICML 2021). Meila, M. & Zhang, T. (eds.). ML Research Press, p. 2880-2891 12 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    3 Citations (Scopus)
  • Learning with Group Noise

    Wang, Q., Yao, J., Gong, C., Liu, T., Gong, M., Yang, H. & Han, B., 18 May 2021, 35th AAAI Conference on Artificial Intelligence, AAAI 2021. AAAI press, p. 10192-10200 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 35, no. 11)(AAAI-21/ IAAI-21/ EAAI-21 Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    5 Citations (Scopus)
  • Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

    Gao, R., Liu, F., Zhang, J., Han, B., Liu, T., Niu, G. & Sugiyama, M., 18 Jul 2021, Proceedings of the 38th International Conference on Machine Learning (ICML 2021). Meila, M. & Zhang, T. (eds.). ML Research Press, p. 3564-3575 12 p. (Proceedings of Machine Learning Research ; vol. 139).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    22 Citations (Scopus)
  • Pointwise Binary Classification with Pairwise Confidence Comparisons

    Feng, L., Shu, S., Lu, N., Han, B., Xu, M., Niu, G., An, B. & Sugiyama, M., 18 Jul 2021, Proceedings of 38th International Conference on Machine Learning (ICML 2021). Meila, M. & Zhang, T. (eds.). ML Research Press, p. 3252-3262 11 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    10 Citations (Scopus)
  • Privacy-Preserving Stochastic Gradual Learning

    Han, B., Tsang, I. W., Xiao, X., Chen, L., Fung, S. F. & Yu, C. P., 1 Aug 2021, In: IEEE Transactions on Knowledge and Data Engineering. 33, 8, p. 3129-3140 12 p.

    Research output: Contribution to journalJournal articlepeer-review

    4 Citations (Scopus)
  • Probabilistic Margins for Instance Reweighting in Adversarial Training

    Wang, Q., Liu, F., HAN, B., Liu, T., Gong, C., Niu, G., Zhou, M. & Sugiyama, M., 6 Dec 2021, 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural Information Processing Systems Foundation, Vol. 28. p. 23258-23269 12 p. (Advances in Neural Information Processing Systems; vol. 34)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    16 Citations (Scopus)
  • Robust early-learning: Hindering the memorization of noisy labels

    Xia, X., Liu, T., Han, B., Gong, C., Wang, N., Ge, Z. & Chang, Y., May 2021, Proceedings of Ninth International Conference on Learning Representations, ICLR 2021. International Conference on Learning Representations, p. 1-15 15 p.

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
  • Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

    Wang, Q., Han, B., Liu, T., Niu, G., Yang, J. & Gong, C., 18 May 2021, 35th AAAI Conference on Artificial Intelligence, AAAI 2021. Association for the Advancement of Artificial Intelligence, p. 10183-10191 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 35, no. 11)(AAAI-21/ IAAI-21/ EAAI-21 Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    13 Citations (Scopus)
  • TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

    Chi, H., Liu, F., Yang, W., Lan, L., Liu, T., Han, B., Cheung, W. K. W. & Kwok, J. T., 6 Dec 2021, 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural Information Processing Systems Foundation, Vol. 25. p. 20970-20982 13 p. (Advances in Neural Information Processing Systems; vol. 34)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    18 Citations (Scopus)
  • Towards Defending against Adversarial Examples via Attack-Invariant Features

    Zhou, D., Liu, T., Han, B., Wang, N., Peng, C. & Gao, X., 18 Jul 2021, Proceedings of 38th International Conference on Machine Learning (ICML 2021). Meila, M. & Zhang, T. (eds.). ML Research Press, p. 12835-12845 11 p. (Proceedings of Machine Learning Research; vol. 139).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    22 Citations (Scopus)
  • Universal Semi-Supervised Learning

    Huang, Z., Xue, C., Han, B., Yang, J. & Gong, C., 6 Dec 2021, 35th Conference on Neural Information Processing Systems (NeurIPS 2021). Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural Information Processing Systems Foundation, Vol. 32. p. 26714-26725 12 p. (Advances in Neural Information Processing Systems; vol. 34)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    24 Citations (Scopus)
  • 2020

    Attacks which do not kill training make adversarial learning stronger

    Zhang, J., Xu, X., Han, B., Niu, G., Cui, L., Sugiyama, M. & Kankanhalli, M., Jul 2020, Proceedings of the 37th International Conference on Machine Learning, ICML 2020. Daumé III, H. & Singh, A. (eds.). ML Research Press, p. 11214-11224 11 p. (Proceedings of Machine Learning Research; vol. 119).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    124 Citations (Scopus)
  • Cross-graph: Robust and unsupervised embedding for attributed graphs with corrupted structure

    Wang, C., Han, B., Pan, S., Jiang, J., Niu, G. & Long, G., Nov 2020, Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020. Plant, C., Wang, H., Cuzzocrea, A., Zaniolo, C. & Wu, X. (eds.). IEEE, p. 571-580 10 p. 9338269. (Proceedings - IEEE International Conference on Data Mining, ICDM; vol. 2020-November).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    5 Citations (Scopus)
  • Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

    Yao, Y., Liu, T., Han, B., Gong, M., Deng, J., Niu, G. & Sugiyama, M., 6 Dec 2020, 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F. & Lin, H. (eds.). Neural Information Processing Systems Foundation, Vol. 9. p. 7260-7271 12 p. (Advances in Neural Information Processing Systems; vol. 33)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    110 Citations (Scopus)
  • Learning with multiple complementary labels

    Feng, L., Kaneko, T., Han, B., Niu, G., An, B. & Sugiyama, M., Jul 2020, Proceedings of the 37th International Conference on Machine Learning, ICML 2020. Daumé III, H. & Singh, A. (eds.). ML Research Press, p. 3053-3062 10 p. (Proceedings of Machine Learning Research; vol. 119).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    42 Citations (Scopus)
  • Part-dependent Label Noise: Towards Instance-dependent Label Noise

    Xia, X., Liu, T., Han, B., Wang, N., Gong, M., Liu, H., Niu, G., Tao, D. & Sugiyama, M., 6 Dec 2020, 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F. & Lin, H. (eds.). Neural Information Processing Systems Foundation, Vol. 10. p. 7597–7610 14 p. (Advances in Neural Information Processing Systems; vol. 33)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    144 Citations (Scopus)
  • Provably consistent partial-label learning

    Feng, L., Lv, J., Han, B., Xu, M., Niu, G., Geng, X., An, B. & Sugiyama, M., 6 Dec 2020, 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F. & Lin, H. (eds.). Neural Information Processing Systems Foundation, Vol. 14. p. 10948-10960 13 p. (Advances in Neural Information Processing Systems; vol. 33)(NeurIPS Proceedings).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    79 Citations (Scopus)
  • Searching to exploit memorization effect in learning with noisy labels

    Yao, Q., Yang, H., Han, B., Niu, G. & Kwok, J. T. Y., Jul 2020, Proceedings of the 37th International Conference on Machine Learning. Daumé III, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 10789-10798 10 p. (Proceedings of Machine Learning Research; vol. 119).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    56 Citations (Scopus)
  • SIGUA: Forgetting may make learning with noisy labels more robust

    Han, B., Niu, G., Yu, X., Yao, Q., Xu, M., Tsang, I. W. & Sugiyama, M., Jul 2020, Proceedings of the 37th International Conference on Machine Learning, ICML 2020. Daumé III, H. & Singh, A. (eds.). ML Research Press, p. 3964-3974 11 p. (Proceedings of Machine Learning Research; vol. 119).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    58 Citations (Scopus)
  • Variational imitation learning with diverse-quality demonstrations

    Tangkaratt, V., Han, B., Khan, M. E. & Sugiyama, M., Jul 2020, Proceedings of the 37th International Conference on Machine Learning, ICML 2020. Daumé III, H. & Singh, A. (eds.). ML Research Press, p. 9349-9359 11 p. (Proceedings of Machine Learning Research; vol. 119).

    Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

    Open Access
    17 Citations (Scopus)