20202024

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  • 2023

    AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning

    Zhang, Y., Zhou, Z., Yao, Q., Chu, X. & Han, B., 4 Aug 2023, KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), p. 3446-3457 12 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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

    Open Access
    6 Citations (Scopus)
  • Adjustment and Alignment for Unbiased Open Set Domain Adaptation

    Li, W., Liu, J., Han, B. & Yuan, Y., 17 Jun 2023, Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023. IEEE, p. 24110-24119 10 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2023-June).

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

    11 Citations (Scopus)
  • A Universal Unbiased Method for Classification from Aggregate Observations

    Wei, Z., Feng, L., Han, B., Liu, T., Niu, G., Zhu, X. & Shen, H. T., Jul 2023, Proceedings of 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, p. 36804-36820 17 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
  • Combating Exacerbated Heterogeneity for Robust Models in Federated Learning

    Zhu, J., Yao, J., Liu, T., Yao, Q., Xu, J. & Han, B., 2 Feb 2023, Proceedings of The Eleventh International Conference on Learning Representations, ICLR 2023. International Conference on Learning Representations, p. 1-34 34 p.

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

    Open Access
  • Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score

    Zhang, S., Liu, F., Yang, J., Yang, Y., Li, C., Han, B. & Tan, M., Jul 2023, Proceedings of 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, p. 41429-41451 23 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
    2 Citations (Scopus)
  • Detecting Out-of-distribution Data through In-distribution Class Prior

    Jiang, X., Liu, F., Fang, Z., Chen, H., Liu, T., Zheng, F. & Han, B., Jul 2023, Proceedings of the 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, Vol. 202. p. 15067-15088 22 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
    1 Citation (Scopus)
  • Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation

    Dong, R., Liu, F., Chi, H., Liu, T., Gong, M., Niu, G., Sugiyama, M. & Han, B., Jul 2023, Proceedings of 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, p. 8260-8275 16 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
    2 Citations (Scopus)
  • Exploring Model Dynamics for Accumulative Poisoning Discovery

    Zhu, J., Guo, X., Yao, J., Du, C., He, L., Yuan, S., Liu, T., Wang, L. & Han, B., Jul 2023, Proceedings of the 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, Vol. 202. p. 42983-43004 22 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
  • Moderately Distributional Exploration for Domain Generalization

    Dai, R., Zhang, Y., Fang, Z., Han, B. & Tian, X., Jul 2023, Proceedings of 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, p. 6786-6817 32 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
    3 Citations (Scopus)
  • NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

    He, X., Yao, J., Wang, Y., Tang, Z., Cheung, K. C., See, S., Han, B. & Chu, X., 27 Jun 2023, Proceedings of the 37th AAAI Conference on Artificial Intelligence. Williams, B., Chen, Y. & Neville, J. (eds.). 1st ed. Washington, DC: AAAI press, p. 7839-7847 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 37, no. 6).

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

    Open Access
    3 Citations (Scopus)
  • On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation

    Zhou, Z., Zhou, C., Li, X., Yao, J., Yao, Q. & Han, B., Jul 2023, Proceedings of the 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, Vol. 202. p. 42843-42877 35 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
    3 Citations (Scopus)
  • Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

    Huang, Z., Zhu, M., Xia, X., Shen, L., Yu, J., Gong, C., Han, B., Du, B. & Liu, T., 17 Jun 2023, Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023. Vancouver, BC, Canada: IEEE, p. 16175-16185 11 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2023-June).

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

    8 Citations (Scopus)
  • Unleashing Mask: Explore the Intrinsic Out-of-distribution Detection Capability

    Zhu, J., Li, H., Yao, J., Liu, T., Xu, J. & Han, B., Jul 2023, Proceedings of the 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, Vol. 202. p. 43068-43104 37 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
  • Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?

    Yao, Y., Gong, M., Du, Y., Yu, J., Han, B., Zhang, K. & Liu, T., Jul 2023, Proceedings of 40th International Conference on Machine Learning, ICML 2023. Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S. & Scarlett, J. (eds.). ML Research Press, p. 39660-39673 14 p. (Proceedings of Machine Learning Research; vol. 202).

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

    Open Access
  • 2022

    Adversarial Robustness through the Lens of Causality

    ZHANG, Y., Gong, M., Liu, T., Niu, G., Tian, X., Han, B., Scholkopf, B. & Zhang, K., 25 Apr 2022, Proceedings of Tenth International Conference on Learning Representations, ICLR 2022. International Conference on Learning Representations, 20 p.

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

    Open Access
  • Adversarial Training with Complementary Labels: On the Necessity of Gradually Informative Attacks

    Zhou, J., Zhu, J., Zhang, J., Liu, T., Niu, G., Han, B. & Sugiyama, M., 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
    1 Citation (Scopus)
  • Bilateral Dependency Optimization: Defending Against Model-inversion Attacks

    Peng, X., Liu, F., Zhang, J., Lan, L., Ye, J., Liu, T. & Han, B., 14 Aug 2022, KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), p. 1358–1367 10 p.

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

    8 Citations (Scopus)
  • Contrastive Learning with Boosted Memorization

    Zhou, Z., Yao, J., Wang, Y., Han, B. & Zhang, Y., 17 Jul 2022, Proceedings of 39th International Conference on Machine Learning (ICML’22). Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G. & Sabato, S. (eds.). ML Research Press, p. 27367-27377 11 p. (Proceedings of Machine Learning Research; vol. 162).

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

    Open Access
    6 Citations (Scopus)
  • Device-Cloud Collaborative Recommendation via Meta Controller

    Yao, J., Wang, F., Ding, X., Chen, S., Han, B., Zhou, J. & Yang, H., Aug 2022, KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), p. 4353–4362 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

    Open Access
    7 Citations (Scopus)
  • EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs

    Ying, G., He, X., Gao, B., Han, B. & Chu, X., 21 Oct 2022, Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVI. Avidan, S., Brostow, G., Cissé, M., Farinella, G. M. & Hassner, T. (eds.). 1st ed. Springer Cham, p. 37–53 17 p. (Lecture Notes in Computer Science; vol. 13676)(ECCV: European Conference on Computer Vision).

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

    Open Access
    7 Citations (Scopus)
  • Exploiting Class Activation Vector for Partial-Label Learning

    Zhang, F., Feng, L., Han, B., Liu, T., Niu, G., Qin, T. & Sugiyama, M., 25 Apr 2022, Proceedings of Tenth International Conference on Learning Representations, ICLR 2022. International Conference on Learning Representations, 17 p.

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

    Open Access
  • Fair Classification with Instance-dependent Label Noise

    Wu, S., Gong, M., Han, B., Liu, Y. & Liu, T., Apr 2022, Proceedings of 1st Conference on Causal Learning and Reasoning, CLeaR 2022. Schölkopf, B., Uhler, C. & Zhang, K. (eds.). Conference on Causal Learning and Reasoning, Vol. 140. p. 1-17 17 p. (Proceedings of Machine Learning Research; vol. 177).

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

    Open Access
    7 Citations (Scopus)
  • Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

    Gao, R., Wang, J., Zhou, K., Liu, F., Xie, B., Niu, G., Han, B. & Cheng, J., 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. 7144-7163 20 p. (Proceedings of Machine Learning Research; vol. 162).

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

    Open Access
    4 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
    33 Citations (Scopus)
  • 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)
  • 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
    12 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
    16 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

    18 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

    21 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
    9 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
    21 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)