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What Make Partial-Label Learning Algorithms Effective?.
In Proceedings of 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, Canada, Dec 10--15, 2024.

Y. Liu*, J. Lv*, X Geng, and N Xu.
Learning with Partial-label and unlabeled data: A uniform treatment for supervision redundancy and insufficiency.
In Proceedings of 41st International Conference on Machine Learning (ICML 2024), PMLR, Vienna, Austria, Jul 21--27, 2024.

S. Xia*, J. Lv*, N. Xu, G. Niu, and X. Geng.
Towards effective visual representations for partial-label learning.
In Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023), pp. 15589--15598, Vancouver, British Columbia, Canada, Jun 18--22, 2023.

N. Xu, B. Liu, J. Lv, C. Qiao, and X. Geng.
Progressive Purification for Instance-Dependent Partial Label Learning.
In Proceedings of 40th International Conference on Machine Learning (ICML 2023), PMLR, vol. 202, pp. 38551--38565, Honolulu, Hawaii, USA, Jul 24--30, 2023.

B. Liu, N. Xu, J. Lv, and X. Geng.
Revisiting Pseudo-Label for Single-Positive Multi-Label Learning.
In Proceedings of 40th International Conference on Machine Learning (ICML 2023), PMLR, vol. 202, pp. 22249--22265, Honolulu, Hawaii, USA, Jul 24--30, 2023.

C. Qiao, N. Xu, J. Lv, Y. Ren, and X. Geng.
FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning.
In Proceedings of 40th International Conference on Machine Learning (ICML 2023), PMLR, vol. 202, pp. 28321--28336, Honolulu, Hawaii, USA, Jul 24--30, 2023.

S. Xia, J. Lv, N. Xu, and X. Geng.
Ambiguity-Induced Contrastive Learning for InstanceDependent Partial Label Learning.
In Proceedings of 31st International Joint Conference on Artificial Intelligence (IJCAI 22), pp. 3615--3621, Vienna, Austria, Jul 23--29, 2022.

J. Lv, M. Xu, L. Feng, G. Niu, X. Geng, and M. Sugiyama.
Progressive identification of true labels for partial-label learning.
In Proceedings of 37th International Conference on Machine Learning (ICML 2020), PMLR, vol. 119, pp. 6500--6510, Online, Jul 12--18, 2020.

L. Feng, J. Lv, B. Han, M. Xu, G. Niu, X. Geng, B. An, and M. Sugiyama.
Provably consistent partial-label learning.
In Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pp. 10948--10960, Online, Dec 6--12, 2020.

J. Lv, N. Xu, R. Zheng, and X. Geng.
Weakly Supervised Multi-Label Learning via Label Enhancement.
In Proceedings of 28th International Joint Conference on Artificial Intelligence (IJCAI 19), pp. 3101--3107, Macao, China, Aug 10--16, 2019.

N. Xu, J. Lv, and X. Geng.
Partial Label Learning via Label Enhancement.
In Proceedings of 33th AAAI Conference on Artificial Intelligence (AAAI 2019), pp. 5557--5564, Honolulu, Hawaii, USA, Jan 27--Feb 1, 2019.

P. Hou, X. Geng, Z. Huo, and J. Lv.
Semi-supervised Adaptive Label Distribution Learning for Facial Age Estimation.
In Proceedings of 31th AAAI Conference on Artificial Intelligence (AAAI 2017), pp. 2015--2021, San Francisco, CA, Feb 4--9, 2017..

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 partial-label learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear.
[ link ]

Z. Wu, J. Lv, and M. Sugiyama.
Learning with Proper Partial Labels.
Neural Computation, vol. 35, no. 1, pp. 58--81, 2023.
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J. Lv, T. Wu, C. Peng, Y. Liu, N. Xu, and X. Geng.
Compact Learning for Multi-Label Classification.
Pattern Recognition, vol. 113, pp. 107833, 2021.
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