The bibtex for the journal and conference articles is available here. The software can be found on this page.

Journal Articles

• du Plessis, M. C., Shiino, H., & Sugiyama, M.
  Online direct density-ratio estimation applied to inlier-based outlier detection.
  Neural Computation, vol.27, no.9, pp.1899-1914, 2015.
  [ Paper]
• du Plessis, M. C. & Sugiyama, M.
  Semi-supervised learning of class balance under class-prior change by distribution matching.
  Neural Networks, vol.50, pp.110-119, 2014.
  [Paper]
• du Plessis, M. C. & Sugiyama, M.
  Class prior estimation from positive and unlabeled data.
  IEICE Transactions on Information and Systems, vol.E97-D, no.5, pp.1358-1362, 2014.
  [Paper]
• Nguyen, T. D., du Plessis, M. C., Kanamori, T., & Sugiyama, M.
  Constrained least-squares density-difference estimation.
  IEICE Transactions on Information and Systems, vol.E97-D, no.7 pp.1822-1829, 2014.
  [Paper]
• Sugiyama, M., Yamada, M., du Plessis, M. C., & Liu, S.
  Learning under non-stationarity: Covariate shift adaptation, class-balance change adaptation, and change detection.
  Journal of the Japan Statistical Society, vol.44, no.1 pp.113-136, 2014.
  [ Paper (in Japanese)]
• Sugiyama, M., Yamada, M., & du Plessis, M. C.
  Learning under non-stationarity: Covariate shift and class-balance change.
  WIREs Computational Statistics, 13 pages, 2013.
  [Paper]
• Sugiyama, M., Suzuki, T., Kanamori, T., du Plessis, M. C., Liu, S., & Takeuchi, I.
  Density-difference estimation.
  Neural Computation, vol.25, no.10, pp.2734-2775, 2013.
  [Paper]
• Sugiyama, M., Liu, S., du Plessis, M. C., Yamanaka, M., Yamada, M., Suzuki, T., & Kanamori, T.
  Direct divergence approximation between probability distributions and its applications in machine learning.
  Journal of Computing Science and Engineering, vol.7, no.2, pp.99-111, 2013.
  [Paper]
• Kawakubo, H., du Plessis, M. C., & Sugiyama, M.
  Computationally efficient class-prior estimation under class balance change using energy distance.
  IEICE Transactions on Information and Systems, vol.E99-D, no.1, pp.176-186, 2016.
  [ Paper]

 

Conference Articles

• Niu, G., du Plessis, M. C., Sakai, T., Ma, Y., & Sugiyama, M.
  Theoretical comparisons of positive-unlabeled learning against positive-negative learning.
  In X.XXX and Y. YYY (Eds.), Advances in Neural Information Processing Systems 29, pp.xxxx-xxxx, 2016 (to appear).
• du Plessis, M. C., Niu, G., & Sugiyama, M.
  Class-prior estimation for learning from positive and unlabeled data.
  Proceedings of the 7th Asian Conference on Machine Learning (ACML2015), JMLR Workshop and Conference Proceedings, vol.45, pp.221-236, Hong Kong, China, Nov. 20-22, 2015.
  [ Paper]
• Nguyen, T. D., du Plessis, M. C., & Sugiyama, M.
  Target shift adaptation in supervised learning.
  Proceedings of the 7th Asian Conference on Machine Learning (ACML2015), JMLR Workshop and Conference Proceedings, vol.45, pp.285-300, Hong Kong, China, Nov. 20-22, 2015.
  [ Paper]
• du Plessis, M. C., Niu, G., & Sugiyama, M.
  Convex formulation for learning from positive and unlabeled data.
  In F. Bach and D. Blei (Eds.), Proceedings of 32nd International Conference on Machine Learning (ICML2015), JMLR Workshop and Conference Proceedings, vol.37, pp.1386-1394, Lille, France, Jul. 6-11, 2015.
  [ Paper]
• du Plessis, M. C., Niu, G., & Sugiyama, M.
  Analysis of learning from positive and unlabeled data.
  In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27, pp.703-711, 2014.
  (Presented at Neural Information Processing Systems (NIPS2014), Montreal, Quebec, Canada, Dec. 8-11, 2014)
  [ Paper]
• Niu, G., Dai, B., du Plessis, M. C., & Sugiyama, M.
  Transductive learning with multi-class volume approximation.
  In E. Xing and T. Jebara (Eds.), Proceedings of 31st International Conference on Machine Learning (ICML2014), JMLR Workshop and Conference Proceedings, vol.32, no.2, pp.1377-1385, Beijing, China, Jun. 21-26, 2014.
  [ Paper]
• du Plessis, M. C., Niu, G., & Sugiyama, M.
  Clustering unclustered data: Unsupervised binary labeling of two datasets having different class balances.
  In Proceedings of Conference on Technologies and Applications of Artificial Intelligence (TAAI2013), pp.1-6, Taipei, Taiwan, Dec. 6-8, 2013.(This paper was selected for Best Paper Award)
  [Paper]
• Sugiyama, M., Suzuki, T., Kanamori, T., du Plessis, M. C., Liu, S., & Takeuchi, I.
  Density-difference estimation.
  In P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25, pp.692-700, 2012. (Presented at Neural Information Processing Systems (NIPS2012), Lake Tahoe, Nevada, USA, Dec. 3-6, 2012)
  [Paper]
• du Plessis, M. C. & Sugiyama, M.
  Semi-supervised learning of class balance under class-prior change by distribution matching.
  In J. Langford and J. Pineau (Eds.), Proceedings of 29th International Conference on Machine Learning (ICML2012), pp.823-830, Edinburgh, Scotland, Jun. 26-Jul. 1, 2012.
  [Paper]

 

Other Publications