研究概要

主要論文

• 1. Zhang, J., Xu, X., Han, B., Niu, G., Cui, L., Sugiyama, M., & Kankanhalli, M.:
  "Attacks which do not kill training make adversarial learning stronger."
  In Proceedings of 37th International Conference on Machine Learning (ICML2020), to appear.
• 2. Tangkaratt, V., Han, B., Khan, M. E., & Sugiyama, M.:
  "Variational imitation learning with diverse-quality demonstrations."
  In Proceedings of 37th International Conference on Machine Learning (ICML2020), to appear.
• 3. Han, B., Niu, G., Yu, X., Yao, Q., Xu, M., Tsang, I., & Sugiyama, M.:
  "SIGUA: Forgetting may make learning with noisy labels more robust."
  In Proceedings of 37th International Conference on Machine Learning (ICML2020), to appear.
• 4. Feng, L., Kaneko, T., Han, B., Niu, G., An, B., & Sugiyama, M.:
  "Learning with multiple complementary labels."
  In Proceedings of 37th International Conference on Machine Learning (ICML2020), to appear.
• 5. Chou, Y.-T., Niu, G., Lin, H.-T., & Sugiyama, M.:
  "Unbiased risk estimators can mislead: A case study of learning with complementary labels."
  In Proceedings of 37th International Conference on Machine Learning (ICML2020), to appear.
• 6. Lv, J., Xu, M., Feng, L., Niu, G., Geng, X., & Sugiyama, M.:
  "Progressive identification of true labels for partial-label learning."
  In Proceedings of 37th International Conference on Machine Learning (ICML2020), to appear.
• 7. Ishida, T., Yamane, I., Sakai, T., Niu, G., & Sugiyama, M.:
  "Do we need zero training loss after achieving zero training error?"
  In Proceedings of 37th International Conference on Machine Learning (ICML2020), to appear.
• 8. Lu, N., Zhang, T., Niu, G., & Sugiyama, M.:
  Mitigating overfitting in supervised classification from two "unlabeled datasets: A consistent risk correction approach."
  In Proceedings of 23rd International Conference on Artificial Intelligence and Statistics (AISTATS2020), pp.1115-1125, 2020.
• 9. Xia, X., Liu, T., Wang, N., Han, B., Gong, C., Niu, G., & Sugiyama, M.:
  "Are anchor points really indispensable in label-noise learning?"
  In Advances in Neural Information Processing Systems 32 (NeurIPS2019), pp.6835-6846, 2019.
• 10. Wu, Y.-H., Charoenphakdee, N., Bao, H., Tangkaratt, V., & Sugiyama, M.:
  "Imitation learning from imperfect demonstration."
  In Proceedings of 36th International Conference on Machine Learning (ICML2019), pp.6818-6827, 2019.

研究成果（プレスリリース）

• 

  2018年11月26日
  正信頼度データからの機械学習

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