Publications
[ Books,
Preprints,
Conference Papers,
Journal Articles,
Theses ]
An asterisk (*) beside authors' names indicates equal contributions.
Books
M. Sugiyama, H. Bao, T. Ishida, N. Lu, T. Sakai, and G. Niu.
Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach,
320 pages, Adaptive Computation and Machine Learning series, The MIT Press, 2022.
(My name is missing from the author list in all retailers due to a system issue of the distributor Penguin Random House;
Their information system can only store up to 5 authors who can be received by the retailers from their metadata feeds)
K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, S. Sabato (Eds.).
Proceedings of 39th International Conference on Machine Learning (ICML 2022),
27723 pages, Proceedings of Machine Learning Research, vol. 162, 2022.
Preprints (no review)
R. Gao, F. Liu, K. Zhou, G. Niu, B. Han, and J. Cheng.
Local reweighting for adversarial training.
[ arXiv ]
Y. Cao, L. Feng, S. Shu, Y. Xu, B. An, G. Niu, and M. Sugiyama.
Multiclass classification from singleclass data with confidences.
[ arXiv ]
X. Xia, T. Liu, B. Han, M. Gong, J. Yu, G. Niu, and M. Sugiyama.
Instance correction for learning with openset noisy labels.
[ arXiv ]
C. Chen*, J. Zhang*, X. Xu, T. Hu, G. Niu, G. Chen, and M. Sugiyama.
Guided interpolation for adversarial training.
[ arXiv ]
J. Zhu*, J. Zhang*, B. Han, T. Liu, G. Niu, H. Yang, M. Kankanhalli, and M. Sugiyama.
Understanding the interaction of adversarial training with noisy labels.
[ arXiv ]
S. Wu*, X. Xia*, T. Liu, B. Han, M. Gong, N. Wang, H. Liu, and G. Niu.
Multiclass classification from noisysimilaritylabeled data.
[ arXiv ]
J. Zhang*, B. Han*, G. Niu, T. Liu, and M. Sugiyama.
Where is the bottleneck of adversarial learning with unlabeled data?
[ arXiv ]
F. Liu, J. Lu, B. Han, G. Niu, G. Zhang, and M. Sugiyama.
Butterfly: A panacea for all difficulties in wildly unsupervised domain adaptation.
[ arXiv ]
C.Y. Hsieh, M. Xu, G. Niu, H.T. Lin, and M. Sugiyama.
A pseudolabel method for coarsetofine multilabel learning with limited supervision.
[ OpenReview ]
M. Xu, B. Li, G. Niu, B. Han, and M. Sugiyama.
Revisiting sample selection approach to positiveunlabeled learning: Turning unlabeled data into positive rather than negative.
[ arXiv ]
M. Xu, G. Niu, B. Han, I. W. Tsang, Z.H. Zhou, and M. Sugiyama.
Matrix cocompletion for multilabel classification with missing features and labels.
[ arXiv ]
Conference Papers (full review)
R. Dong*, F. Liu*, H. Chi, T. Liu, M. Gong, G. Niu, M. Sugiyama, and B. Han.
Diversityenhancing generative network for fewshot hypothesis adaptation.
In Proceedings of 40th International Conference on Machine Learning (ICML 2023),
to appear.
[ paper ]
H. Wei, H. Zhuang, R. Xie, L. Feng, G. Niu, B. An, and Y. Li.
Mitigating memorization of noisy labels by clipping the model prediction.
In Proceedings of 40th International Conference on Machine Learning (ICML 2023),
to appear.
[ paper ]
Z. Wei, L. Feng, B. Han, T. Liu, G. Niu, X. Zhu, and H. Shen.
A universal unbiased method for classification from aggregate observations.
In Proceedings of 40th International Conference on Machine Learning (ICML 2023),
to appear.
[ paper ]
S. Xia*, J. Lv*, N. Xu, G. Niu, and X. Geng.
Towards effective visual representations for partiallabel learning.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR 2023),
to appear.
[ paper ]
T. Ishida, I. Yamane, N. Charoenphakdee, G. Niu, and M. Sugiyama.
Is the performance of my deep network too good to be true? A direct approach to estimating the Bayes error in binary classification.
In Proceedings of 11th International Conference on Learning Representations (ICLR 2023),
to appear.
(This paper was selected for oral presentation;
orals : acceptance : submissions = 90 : 1579 : 4966)
[ OpenReview ]
J. Zhou*, J. Zhu*, J. Zhang, T. Liu, G. Niu, B. Han, and M. Sugiyama.
Adversarial training with complementary labels: On the benefit of gradually informative attacks.
In Advances in Neural Information Processing Systems 35 (NeurIPS 2022),
to appear.
[ paper ]
S. Chen, C. Gong, J. Li, J. Yang, G. Niu, and M. Sugiyama.
Learning contrastive embedding in lowdimensional space.
In Advances in Neural Information Processing Systems 35 (NeurIPS 2022),
to appear.
[ paper ]
Y. Cao, T. Cai, L. Feng, L. Gu, J. Gu, B. An, G. Niu, and M. Sugiyama.
Generalizing consistent multiclass classification with rejection to be compatible with arbitrary losses.
In Advances in Neural Information Processing Systems 35 (NeurIPS 2022),
to appear.
[ paper ]
S. Yang, E. Yang, B. Han, Y. Liu, M. Xu, G. Niu, and T. Liu.
Estimating instancedependent Bayeslabel transition matrix using a deep neural network.
In Proceedings of 39th International Conference on Machine Learning (ICML 2022),
PMLR, vol. 162, pp. 2530225312, Baltimore, Maryland, USA, Jul 1723, 2022.
[ paper ]
J. Wei, H. Liu, T. Liu, G. Niu, M. Sugiyama, and Y. Liu.
To smooth or not? When label smoothing meets noisy labels.
In Proceedings of 39th International Conference on Machine Learning (ICML 2022),
PMLR, vol. 162, pp. 2358923614, Baltimore, Maryland, USA, Jul 1723, 2022.
[ paper ]
R. Gao, J. Wang, K. Zhou, F. Liu, B. Xie, G. Niu, B. Han, and J. Cheng.
Fast and reliable evaluation of adversarial robustness with minimummargin attack.
In Proceedings of 39th International Conference on Machine Learning (ICML 2022),
PMLR, vol. 162, pp. 71447163, Baltimore, Maryland, USA, Jul 1723, 2022.
[ paper ]
D. Cheng, T. Liu, Y. Ning, N. Wang, B. Han, G. Niu, X. Gao, and M. Sugiyama.
Instancedependent labelnoise learning with manifoldregularized transition matrix estimation.
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 (CVPR 2022),
pp. 1663016639, New Orleans, Louisiana, USA, Jun 1924, 2022.
[ paper ]
H. Wang, R. Xiao, Y. Li, L. Feng, G. Niu, G. Chen, and J. Zhao.
PiCO: Contrastive label disambiguation for partial label learning.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
18 pages, Online, Apr 2529, 2022.
(This paper was selected for oral presentation;
orals : acceptance : submissions = 55 : 1094 : 3391)
(In addition, this paper received Outstanding Paper Honorable Mention)
[ paper,
OpenReview ]
H. Chi*, F. Liu*, W. Yang, L. Lan, T. Liu, B. Han, G. Niu, M. Zhou, and M. Sugiyama.
Meta discovery: Learning to discover novel classes given very limited data.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
20 pages, Online, Apr 2529, 2022.
(This paper was selected for spotlight presentation;
spotlights : acceptance : submissions = 176 : 1094 : 3391)
[ paper,
OpenReview ]
Y. Yao, T. Liu, B. Han, M. Gong, G. Niu, M. Sugiyama, and D. Tao.
Rethinking classprior estimation for positiveunlabeled learning.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
21 pages, Online, Apr 2529, 2022.
[ paper,
OpenReview ]
J. Wei, Z. Zhu, H. Cheng, T. Liu, G. Niu, and Yang Liu.
Learning with noisy labels revisited: A study using realworld human annotations.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
23 pages, Online, Apr 2529, 2022.
[ CIFARN Dataset,
paper,
OpenReview ]
F. Zhang, L. Feng, B. Han, T. Liu, G. Niu, T. Qin, and M. Sugiyama.
Exploiting class activation value for partiallabel learning.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
17 pages, Online, Apr 2529, 2022.
[ paper,
OpenReview ]
J. Zhu, J. Yao, B. Han, J. Zhang, T. Liu, G. Niu, J. Zhou, J. Xu, and H. Yang.
Reliable adversarial distillation with unreliable teachers.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
15 pages, Online, Apr 2529, 2022.
[ paper,
OpenReview ]
N. Lu, Z. Wang, X. Li, G. Niu, Q. Dou, and M. Sugiyama.
Federated learning from only unlabeled data with classconditionalsharing clients.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
22 pages, Online, Apr 2529, 2022.
[ paper,
OpenReview ]
Y. Zhang, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, and K. Zhang.
CausalAdv: Adversarial robustness through the lens of causality.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
20 pages, Online, Apr 2529, 2022.
[ paper,
OpenReview ]
X. Xia, T. Liu, B. Han, M. Gong, J. Yu, G. Niu, and M. Sugiyama.
Sample selection with uncertainty of losses for learning with noisy labels.
In Proceedings of 10th International Conference on Learning Representations (ICLR 2022),
23 pages, Online, Apr 2529, 2022.
[ paper,
OpenReview ]
Y. Bai*, E. Yang*, B. Han, Y. Yang, J. Li, Y. Mao, G. Niu, and T. Liu.
Understanding and improving early stopping for learning with noisy labels.
In Advances in Neural Information Processing Systems 34 (NeurIPS 2021),
pp. 2439224403, Online, Dec 614, 2021.
[ paper ]
Q. Wang*, F. Liu*, B. Han, T. Liu, C. Gong, G. Niu, M. Zhou, and M. Sugiyama.
Probabilistic margins for instance reweighting in adversarial training.
In Advances in Neural Information Processing Systems 34 (NeurIPS 2021),
pp. 2325823269, Online, Dec 614, 2021.
[ paper ]
Y. Yao, T. Liu, M. Gong, B. Han, G. Niu, and K. Zhang.
Instancedependent labelnoise learning under a structural causal model.
In Advances in Neural Information Processing Systems 34 (NeurIPS 2021),
pp. 44094420, Online, Dec 614, 2021.
[ paper ]
L. Feng, S. Shu, Y. Cao, L. Tao, H. Wei, T. Xiang, B. An, and G. Niu.
Multipleinstance learning from similar and dissimilar bags.
In Proceedings of 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021),
pp. 374382, Online, Aug 1418, 2021.
[ paper ]
S. Chen, G. Niu, C. Gong, J. Li, J. Yang, and M. Sugiyama.
Largemargin contrastive learning with distance polarization regularizer.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 16731683, Online, Jul 1824, 2021.
[ paper ]
H. Yan, J. Zhang, G. Niu, J. Feng, V. Y. F. Tan, and M. Sugiyama.
CIFS: Improving adversarial robustness of CNNs via channelwise importancebased feature selection.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 1169311703, Online, Jul 1824, 2021.
[ paper ]
R. Gao*, F. Liu*, J. Zhang*, B. Han, T. Liu, G. Niu, and M. Sugiyama.
Maximum mean discrepancy test is aware of adversarial attacks.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 35643575, Online, Jul 1824, 2021.
[ paper ]
X. Li, T. Liu, B. Han, G. Niu, and M. Sugiyama.
Provably endtoend labelnoise learning without anchor points.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 64036413, Online, Jul 1824, 2021.
[ paper ]
X. Du*, J. Zhang*, B. Han, T. Liu, Y. Rong, G. Niu, J. Huang, and M. Sugiyama.
Learning diversestructured networks for adversarial robustness.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 28802891, Online, Jul 1824, 2021.
[ paper ]
A. Berthon, B. Han, G. Niu, T. Liu, and M. Sugiyama.
Confidence scores make instancedependent labelnoise learning possible.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 825836, Online, Jul 1824, 2021.
[ paper ]
Y. Zhang, G. Niu, and M. Sugiyama.
Learning noise transition matrix from only noisy labels via total variation regularization.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 1250112512, Online, Jul 1824, 2021.
[ paper ]
L. Feng, S. Shu, N. Lu, B. Han, M. Xu, G. Niu, B. An, and M. Sugiyama.
Pointwise binary classification with pairwise confidence comparisons.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 32523262, Online, Jul 1824, 2021.
[ paper ]
N. Lu*, S. Lei*, G. Niu, I. Sato, and M. Sugiyama.
Binary classification from multiple unlabeled datasets via surrogate set classification.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 71347144, Online, Jul 1824, 2021.
[ paper ]
Y. Cao, L. Feng, Y. Xu, B. An, G. Niu, and M. Sugiyama.
Learning from similarityconfidence data.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 12721282, Online, Jul 1824, 2021.
[ paper ]
S. Wu*, X. Xia*, T. Liu, B. Han, M. Gong, N. Wang, H. Liu, and G. Niu.
Class2Simi: A noise reduction perspective on learning with noisy labels.
In Proceedings of 38th International Conference on Machine Learning (ICML 2021),
PMLR, vol. 139, pp. 1128511295, Online, Jul 1824, 2021.
[ paper ]
J. Zhang, J. Zhu, G. Niu, B. Han, M. Sugiyama, and M. Kankanhalli.
Geometryaware instancereweighted adversarial training.
In Proceedings of 9th International Conference on Learning Representations (ICLR 2021),
29 pages, Online, May 37, 2021.
(This paper was selected for oral presentation;
orals : acceptance : submissions = 53 : 860 : 2997)
[ paper,
OpenReview ]
A. Jacovi, G. Niu, Y. Goldberg, and M. Sugiyama.
Scalable evaluation and improvement of document set expansion via neural positiveunlabeled learning.
In Proceedings of 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021),
pp. 581592, Online, Apr 1923, 2021.
[ paper ]
Q. Wang, B. Han, T. Liu, G. Niu, J. Yang, and C. Gong.
Tackling instancedependent label noise via a universal probabilistic model.
In Proceedings of 35th AAAI Conference on Artificial Intelligence (AAAI 2021),
pp. 1018310191, Online, Feb 29, 2021.
[ paper ]
X. Xia, T. Liu, B. Han, N. Wang, M. Gong, H. Liu, G. Niu, D. Tao, and M. Sugiyama.
Partdependent label noise: Towards instancedependent label noise.
In Advances in Neural Information Processing Systems 33 (NeurIPS 2020),
pp. 75977610, Online, Dec 612, 2020.
(This paper was selected for spotlight presentation;
spotlights : acceptance : submissions = 280 : 1900 : 9454)
[ paper ]
T. Fang*, N. Lu*, G. Niu, and M. Sugiyama.
Rethinking importance weighting for deep learning under distribution shift.
In Advances in Neural Information Processing Systems 33 (NeurIPS 2020),
pp. 1199612007, Online, Dec 612, 2020.
(This paper was selected for spotlight presentation;
spotlights : acceptance : submissions = 280 : 1900 : 9454)
[ paper ]
Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama.
Dual T: Reducing estimation error for transition matrix in labelnoise learning.
In Advances in Neural Information Processing Systems 33 (NeurIPS 2020),
pp. 72607271, Online, Dec 612, 2020.
[ paper ]
L. Feng, J. Lv, B. Han, M. Xu, G. Niu, X. Geng, B. An, and M. Sugiyama.
Provably consistent partiallabel learning.
In Advances in Neural Information Processing Systems 33 (NeurIPS 2020),
pp. 1094810960, Online, Dec 612, 2020.
[ paper ]
C. Wang, B. Han, S. Pan, J. Jiang, G. Niu, and G. Long.
Crossgraph: Robust and unsupervised embedding for attributed graphs with corrupted structure.
In Proceedings of 20th IEEE International Conference on Data Mining (ICDM 2020),
pp. 571580, Online, Nov 1720, 2020.
[ paper ]
J. Zhang*, X. Xu*, B. Han, G. Niu, L. Cui, M. Sugiyama, and M. Kankanhalli.
Attacks which do not kill training make adversarial learning stronger.
In Proceedings of 37th International Conference on Machine Learning (ICML 2020),
PMLR, vol. 119, pp. 1127811287, Online, Jul 1218, 2020.
[ paper ]
B. Han, G. Niu, X. Yu, Q. Yao, M. Xu, I. W. Tsang, and M. Sugiyama.
SIGUA: Forgetting may make learning with noisy labels more robust.
In Proceedings of 37th International Conference on Machine Learning (ICML 2020),
PMLR, vol. 119, pp. 40064016, Online, Jul 1218, 2020.
[ paper ]
L. Feng*, T. Kaneko*, B. Han, G. Niu, B. An, and M. Sugiyama.
Learning with multiple complementary labels.
In Proceedings of 37th International Conference on Machine Learning (ICML 2020),
PMLR, vol. 119, pp. 30723081, Online, Jul 1218, 2020.
[ paper ]
Y.T. Chou, G. Niu, H.T. Lin, and M. Sugiyama.
Unbiased risk estimators can mislead: A case study of learning with complementary labels.
In Proceedings of 37th International Conference on Machine Learning (ICML 2020),
PMLR, vol. 119, pp. 19291938, Online, Jul 1218, 2020.
[ paper ]
Q. Yao, H. Yang, B. Han, G. Niu, and J. T. Kwok.
Searching to exploit memorization effect in learning with noisy labels.
In Proceedings of 37th International Conference on Machine Learning (ICML 2020),
PMLR, vol. 119, pp. 1078910798, Online, Jul 1218, 2020.
[ paper ]
J. Lv, M. Xu, L. Feng, G. Niu, X. Geng, and M. Sugiyama.
Progressive identification of true labels for partiallabel learning.
In Proceedings of 37th International Conference on Machine Learning (ICML 2020),
PMLR, vol. 119, pp. 65006510, Online, Jul 1218, 2020.
[ paper ]
T. Ishida, I. Yamane, T. Sakai, G. Niu, and M. Sugiyama.
Do we need zero training loss after achieving zero training error?
In Proceedings of 37th International Conference on Machine Learning (ICML 2020),
PMLR, vol. 119, pp. 46044614, Online, Jul 1218, 2020.
[ paper ]
N. Lu, T. Zhang, G. Niu, and M. Sugiyama.
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 (AISTATS 2020),
PMLR, vol. 108, pp. 11151125, Online, Aug 2628, 2020.
[ paper ]
C. Li, M. E. Khan, Z. Sun, G. Niu, B. Han, S. Xie, and Q. Zhao.
Beyond unfolding: Exact recovery of latent convex tensor decomposition under reshuffling.
In Proceedings of 34th AAAI Conference on Artificial Intelligence (AAAI 2020),
pp. 46024609, New York, New York, USA, Feb 712, 2020.
[ paper ]
L. Xu, J. Honda, G. Niu, and M. Sugiyama.
Uncoupled regression from pairwise comparison data.
In Advances in Neural Information Processing Systems 32 (NeurIPS 2019),
pp. 39924002, Vancouver, British Columbia, Canada, Dec 814, 2019.
[ paper ]
X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama.
Are anchor points really indispensable in labelnoise learning?
In Advances in Neural Information Processing Systems 32 (NeurIPS 2019),
pp. 68386849, Vancouver, British Columbia, Canada, Dec 814, 2019.
[ paper ]
Y.G. Hsieh, G. Niu, and M. Sugiyama.
Classification from positive, unlabeled and biased negative data.
In Proceedings of 36th International Conference on Machine Learning (ICML 2019),
PMLR, vol. 97, pp. 28202829, Long Beach, California, USA, Jun 915, 2019.
[ paper ]
T. Ishida, G. Niu, A. K. Menon, and M. Sugiyama.
Complementarylabel learning for arbitrary losses and models.
In Proceedings of 36th International Conference on Machine Learning (ICML 2019),
PMLR, vol. 97, pp. 29712980, Long Beach, California, USA, Jun 915, 2019.
[ paper ]
X. Yu, B. Han, J. Yao, G. Niu, I. W. Tsang, and M. Sugiyama.
How does disagreement help generalization against label corruption?
In Proceedings of 36th International Conference on Machine Learning (ICML 2019),
PMLR, vol. 97, pp. 71647173, Long Beach, California, USA, Jun 915, 2019.
[ paper ]
N. Lu, G. Niu, A. K. Menon, and M. Sugiyama.
On the minimal supervision for training any binary classifier from only unlabeled data.
In Proceedings of 7th International Conference on Learning Representations (ICLR 2019),
18 pages, New Orleans, Louisiana, USA, May 69, 2019.
[ paper,
OpenReview ]
T. Ishida, G. Niu, and M. Sugiyama.
Binary classification from positiveconfidence data.
In Advances in Neural Information Processing Systems 31 (NeurIPS 2018),
pp. 59175928, Montreal, Quebec, Canada, Dec 28, 2018.
(This paper was selected for spotlight presentation;
spotlights : acceptance : submissions = 168 : 1011 : 4856)
[ paper ]
B. Han*, J. Yao*, G. Niu, M. Zhou, I. W. Tsang, Y. Zhang, and M. Sugiyama.
Masking: A new perspective of noisy supervision.
In Advances in Neural Information Processing Systems 31 (NeurIPS 2018),
pp. 58365846, Montreal, Quebec, Canada, Dec 28, 2018.
[ paper ]
B. Han*, Q. Yao*, X. Yu, G. Niu, M. Xu, W. Hu, I. W. Tsang, and M. Sugiyama.
Coteaching: Robust training of deep neural networks with extremely noisy labels.
In Advances in Neural Information Processing Systems 31 (NeurIPS 2018),
pp. 85278537, Montreal, Quebec, Canada, Dec 28, 2018.
[ paper ]
S.J. Huang, M. Xu, M.K. Xie, M. Sugiyama, G. Niu, and S. Chen.
Active feature acquisition with supervised matrix completion.
In Proceedings of 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018),
pp. 15711579, London, UK, Aug 1923, 2018.
[ paper ]
W. Hu, G. Niu, I. Sato, and M. Sugiyama.
Does distributionally robust supervised learning give robust classifiers?
In Proceedings of 35th International Conference on Machine Learning (ICML 2018),
PMLR, vol. 80, pp. 20292037, Stockholm, Sweden, Jul 1015, 2018.
[ paper ]
H. Bao, G. Niu, and M. Sugiyama.
Classification from pairwise similarity and unlabeled data.
In Proceedings of 35th International Conference on Machine Learning (ICML 2018),
PMLR, vol. 80, pp. 452461, Stockholm, Sweden, Jul 1015, 2018.
[ paper ]
R. Kiryo, G. Niu, M. C. du Plessis, and M. Sugiyama.
Positiveunlabeled learning with nonnegative risk estimator.
In Advances in Neural Information Processing Systems 30 (NeurIPS 2017),
pp. 16741684, Long Beach, California, USA, Dec 49, 2017.
(This paper was selected for oral presentation;
orals : acceptance : submissions = 40 : 678 : 3240)
[ paper ]
T. Ishida, G. Niu, W. Hu, and M. Sugiyama.
Learning from complementary labels.
In Advances in Neural Information Processing Systems 30 (NeurIPS 2017),
pp. 56445654, Long Beach, California, USA, Dec 49, 2017.
[ paper ]
H. Shiino, H. Sasaki, G. Niu, and M. Sugiyama.
Whiteningfree leastsquares nonGaussian component analysis.
In Proceedings of 9th Asian Conference on Machine Learning (ACML 2017),
PMLR, vol. 77, pp. 375390, Seoul, Korea, Nov 1517, 2017.
(This paper received Best Paper Runnerup Award)
[ paper ]
T. Sakai, M. C. du Plessis, G. Niu, and M. Sugiyama.
Semisupervised classification based on classification from positive and unlabeled data.
In Proceedings of 34th International Conference on Machine Learning (ICML 2017),
PMLR, vol. 70, pp. 29983006, Sydney, Australia, Aug 611, 2017.
[ paper ]
G. Niu, M. C. du Plessis, T. Sakai, Y. Ma, and M. Sugiyama.
Theoretical comparisons of positiveunlabeled learning against positivenegative learning.
In Advances in Neural Information Processing Systems 29 (NeurIPS 2016),
pp. 11991207, Barcelona, Spain, Dec 510, 2016.
[ paper ]
H. Sasaki, G. Niu, and M. Sugiyama.
NonGaussian component analysis with logdensity gradient estimation.
In Proceedings of 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016),
PMLR, vol. 51, pp. 11771185, Cadiz, Spain, May 911, 2016.
[ paper ]
T. Zhao, G. Niu, N. Xie, J. Yang, and M. Sugiyama.
Regularized policy gradients: Direct variance reduction in policy gradient estimation.
In Proceedings of 7th Asian Conference on Machine Learning (ACML 2015),
PMLR, vol. 45, pp. 333348, Hong Kong, China, Nov 2022, 2015.
[ paper ]
M. C. du Plessis, G. Niu, and M. Sugiyama.
Classprior estimation for learning from positive and unlabeled data.
In Proceedings of 7th Asian Conference on Machine Learning (ACML 2015),
PMLR, vol. 45, pp. 221236, Hong Kong, China, Nov 2022, 2015.
[ paper ]
M. C. du Plessis, G. Niu, and M. Sugiyama.
Convex formulation for learning from positive and unlabeled data.
In Proceedings of 32nd International Conference on Machine Learning (ICML 2015),
PMLR, vol. 37, pp. 13861394, Lille, France, Jul 611, 2015.
[ paper ]
M. C. du Plessis, G. Niu, and M. Sugiyama.
Analysis of learning from positive and unlabeled data.
In Advances in Neural Information Processing Systems 27 (NeurIPS 2014),
pp. 703711, Montreal, Quebec, Canada, Dec 813, 2014.
[ paper ]
G. Niu, B. Dai, M. C. du Plessis, and M. Sugiyama.
Transductive learning with multiclass volume approximation.
In Proceedings of 31st International Conference on Machine Learning (ICML 2014),
PMLR, vol. 32, no. 2, pp. 13771385, Beijing, China, Jun 2126, 2014.
[ paper ]
M. C. du Plessis, G. Niu, and M. Sugiyama.
Clustering unclustered data: Unsupervised binary labeling of two datasets having different class balances.
In Proceedings of 2013 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2013),
pp. 16, Taipei, Taiwan, Dec 68, 2013.
(This paper received Best Paper Award)
[ paper ]
G. Niu, W. Jitkrittum, B. Dai, H. Hachiya, and M. Sugiyama.
Squaredloss mutual information regularization: A novel informationtheoretic approach to semisupervised learning.
In Proceedings of 30th International Conference on Machine Learning (ICML 2013),
PMLR, vol. 28, no. 3, pp. 1018, Atlanta, Georgia, USA, Jun 1621, 2013.
[ paper ]
G. Niu, B. Dai, M. Yamada, and M. Sugiyama.
Informationtheoretic semisupervised metric learning via entropy regularization.
In Proceedings of 29th International Conference on Machine Learning (ICML 2012),
pp. 8996, Edinburgh, Scotland, Jun 26Jul 1, 2012.
[ paper ]
T. Zhao, H. Hachiya, G. Niu, and M. Sugiyama.
Analysis and improvement of policy gradient estimation.
In Advances in Neural Information Processing Systems 24 (NeurIPS 2011),
pp. 262270, Granada, Spain, Dec 1217, 2011.
[ paper ]
M. Yamada, G. Niu, J. Takagi, and M. Sugiyama.
Computationally efficient sufficient dimension reduction via squaredloss mutual information.
In Proceedings of 3rd Asian Conference on Machine Learning (ACML 2011),
PMLR, vol. 20, pp. 247262, Taoyuan, Taiwan, Nov 1315, 2011.
[ paper ]
G. Niu, B. Dai, L. Shang, and M. Sugiyama.
Maximum volume clustering.
In Proceedings of 14th International Conference on Artificial Intelligence and Statistics (AISTATS 2011),
PMLR, vol. 15, pp. 561569, Fort Lauderdale, Florida, USA, Apr 1113, 2011.
[ paper ]
B. Dai, B. Hu, and G. Niu.
Bayesian maximum margin clustering.
In Proceedings of 10th IEEE International Conference on Data Mining (ICDM 2010),
pp. 108117, Sydney, Australia, Dec 1417, 2010.
[ paper ]
G. Niu, B. Dai, Y. Ji, and L. Shang.
Rough margin based core vector machine.
In Proceedings of 14th PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD 2010),
LNCS, vol. 6118, pp. 134141, Hyderabad, India, Jun 2124, 2010.
[ paper ]
B. Dai and G. Niu.
Compact margin machine.
In Proceedings of 14th PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD 2010),
LNCS, vol. 6119, pp. 507514, Hyderabad, India, Jun 2124, 2010.
[ paper ]
Journal Articles
J. Lv, B. Liu, L. Feng, N. Xu, M. Xu, B. An, G. Niu, X. Geng, and M. Sugiyama.
On the robustness of average losses for partiallabel learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear.
[ link ]
L. Feng, S. Shu, Y. Cao, L. Tao, H. Wei, T. Xiang, B. An, and G. Niu.
Multipleinstance learning from unlabeled bags with pairwise similarity.
IEEE Transactions on Knowledge and Data Engineering, to appear.
[ link ]
C. Gong, Y. Ding, B. Han, G. Niu, J. Yang, J. You, D. Tao, and M. Sugiyama.
Classwise denoising for robust learning under label noise.
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 28352848, 2023.
[ link ]
T. Zhao, Y. Wang, W. Sun, Y. Chen, G. Niu, and M. Sugiyama.
Representation learning for continuous action spaces is beneficial for efficient policy learning.
Neural Networks, vol. 159, pp. 137152, 2023.
[ link ]
S. Wu, T. Liu, B. Han, J. Yu, G. Niu, and M. Sugiyama.
Learning from noisy pairwise similarity and unlabeled data.
Journal of Machine Learning Research, vol. 23, no. 307, pp. 134, 2022.
[ link ]
Z. Wang, J. Jiang, B. Han, L. Feng, B. An, G. Niu, and G. Long.
SemiNLL: A framework of noisylabel learning by semisupervised learning.
Transactions on Machine Learning Research, 07/2022, 25 pages, 2022.
[ link ]
J. Zhang*, X. Xu*, B. Han, T. Liu, L. Cui, G. Niu, and M. Sugiyama.
NoiLIn: Improving adversarial training and correcting stereotype of noisy labels.
Transactions on Machine Learning Research, 06/2022, 25 pages, 2022.
[ link ]
Y. Pan, I. W. Tsang, W. Chen, G. Niu, and M. Sugiyama.
Fast and robust rank aggregation against model misspecification.
Journal of Machine Learning Research, vol. 23, no. 23, pp. 135, 2022.
[ link ]
W. Xu, G. Niu, A. Hyvärinen, and M. Sugiyama.
Direction matters: On influencepreserving graph summarization and maxcut principle for directed graphs.
Neural Computation, vol. 33, no. 8, pp. 21282162, 2021.
[ link ]
T. Sakai, G. Niu, and M. Sugiyama.
Informationtheoretic representation learning for positiveunlabeled classification.
Neural Computation, vol. 33, no. 1, pp. 244268, 2021.
[ link ]
H. Sasaki, T. Kanamori, A. Hyvärinen, G. Niu, and M. Sugiyama.
Modeseeking clustering and density ridge estimation via direct estimation of densityderivativeratios.
Journal of Machine Learning Research, vol. 18, no. 180, pp. 145, 2018.
[ link ]
T. Sakai, G. Niu, and M. Sugiyama.
Semisupervised AUC optimization based on positiveunlabeled learning.
Machine Learning, vol. 107, no. 4, pp. 767794, 2018.
[ link ]
H. Sasaki, V. Tangkaratt, G. Niu, and M. Sugiyama.
Sufficient dimension reduction via direct estimation of the gradients of logarithmic conditional densities.
Neural Computation, vol. 30, no. 2, pp. 477504, 2018.
[ link ]
M. C. du Plessis*, G. Niu*, and M. Sugiyama.
Classprior estimation for learning from positive and unlabeled data.
Machine Learning, vol. 106, no. 4, pp. 463492, 2017.
[ link ]
H. Sasaki, Y.K. Noh, G. Niu, and M. Sugiyama.
Direct densityderivative estimation.
Neural Computation, vol. 28, no. 6, pp. 11011140, 2016.
[ link ]
G. Niu, B. Dai, M. Yamada, and M. Sugiyama.
Informationtheoretic semisupervised metric learning via entropy regularization.
Neural Computation, vol. 26, no. 8, pp. 17171762, 2014.
[ link ]
D. Calandriello, G. Niu, and M. Sugiyama.
Semisupervised informationmaximization clustering.
Neural Networks, vol. 57, pp. 103111, 2014.
[ link ]
M. Sugiyama, G. Niu, M. Yamada, M. Kimura, and H. Hachiya.
Informationmaximization clustering based on squaredloss mutual information.
Neural Computation, vol. 26, no. 1, pp. 84131, 2014.
[ link ]
G. Niu, B. Dai, L. Shang, and M. Sugiyama.
Maximum volume clustering: A new discriminative clustering approach.
Journal of Machine Learning Research, vol. 14 (Sep), pp. 26412687, 2013.
[ link ]
T. Zhao, H. Hachiya, G. Niu, and M. Sugiyama.
Analysis and improvement of policy gradient estimation.
Neural Networks, vol. 26, pp. 118129, 2012.
[ link ]
Y. Ji, J. Chen, G. Niu, L. Shang, and X. Dai.
Transfer learning via multiview principal component analysis.
Journal of Computer Science and Technology, vol. 26, no. 1, pp. 8198, 2011.
[ link ]
Theses
Gang Niu.
Discriminative methods with imperfect supervision in machine learning (204 pages).
Doctoral Thesis, Department of Computer Science, Tokyo Institute of Technology, Sep 2013.
Gang Niu.
Support vector learning based on rough set modeling (71 pages in Chinese).
Master Thesis, Department of Computer Science and Technology, Nanjing University, May 2010.
