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Breaking the scale barrier: One-shot knowledge transfer via frequency transform.
In Proceedings of 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea, Jul 6--11, 2026.

J. Lv, Z. Zhang, C. Yang, S. Xia, N. Xu, X. Geng.
When labelers stay silent: The power of ties in cost-effective preference learning.
In Proceedings of 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea, Jul 6--11, 2026.

S. Guo*, J. Lv*, Z. Kou, S. Lin, X. Geng.
FedPAT: Federated test-time adaptation via prototype affinity topology.
In Proceedings of 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea, Jul 6--11, 2026.

Z. Zhang, Q. Tao, J. Lv, N. Zhao, L. Feng, J. T. Zhou.
TokenSwap: Backdoor attack on the compositional understanding of large vision-language models.
In Proceedings of 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea, Jul 6--11, 2026.

R. Li, J. Xiong, X. He, J. Zhao, J.i Lv, H. Fang, L. Qi, X. Wang.
ChatHLS: Towards systematic design automation and optimization for high-level synthesis.
In Proceedings of 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), San Diego, CA, Jul 2--7, 2026.

J. Shen, F. Feng, J. Xu, Y. Xie, J. Lv#, X. Geng#.
A unified framework for knowledge transfer in bidirectional model scaling.
In Proceedings of 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026), Denver, CO, Jun 3--7, 2026.

J. Xu, S. Xia, J. Lv#, X. Geng.
Unlocking pre-trained weights: Parameter inheritance for zero-shot initialization.
In Proceedings of 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026), Denver, CO, Jun 3--7, 2026.

J. Xu, S. Xia, X. Yang, J. Lv#, X. Geng.
Learngene tells you how to customize: Task-aware parameter initialization at flexible scales.
In Proceedings of 42nd International Conference on Machine Learning (ICML 2025), Vancouver, Canada, Jul 13--19, 2025.

J. Lv, Y. Liu, S. Xia, N. Xu, M. Xu, G. Niu, M. Zhang, M. Sugiyama, X. Geng.
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), pp. 31614--31628, 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.
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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.
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L. Feng, J. Lv, B. Han, M. Xu, G. Niu, X. Geng, B. An, and M. Sugiyama.
Provably consistent partial-label learning.
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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.
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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.
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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.
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