Ph.D., Associate Professor

   School of Computer Science and Engineering

   Southeast University

   Email: is.jiaqi.lv@gmail.com & jiaqi.lv@seu.edu.cn

   I am a member of PAtttern Learning and Mining (PALM) Lab.

吕佳祺，博士，东南大学计算机科学与工程学院副教授，东南大学“紫金青年学者”。2021年于东南大学获得博士学位，2021-2023年任日本理化学研究所AIP中心博士后研究员。研究领域包括机器学习、深度学习，重点关注可信赖人工智能，当前的工作重点是从弱监督数据中进行学习和推理，旨在减少对大量标注数据的依赖，同时不牺牲模型的可靠性。在相关领域的国际顶级会议与期刊上（包括TPAMI, ICML, NeurIPS等）发表论文10余篇，并担任多个国际著名会议的程序员会委员、审稿人。

I'm currently an Associate Researcher at PALM Lab, the School of Computer Science and Engineering, Southeast University (SEU), Nanjing, China. My research interests lie in machine learning and deep learning, focusing on trustworthy AI. With a current emphasis on learning and reasoning from weakly supervised data, we aim to reducing our reliance on extensively annotated datasets without sacrificing the models' reliability. The ultimate goal is to make machine learning a tool for enhancing human decisions without compromising our ethical values or privacy. I received my Bachelor's and Doctorate degrees at SEU, under the guidance of Prof. Xin Geng. Following my graduation in 2021, I embarked on a postdoctoral journey at the RIKEN Center for Advanced Intelligence Project, Japan, collaborating with esteemed scholars such as Prof. Masashi Sugiyama and Dr. Gang Niu. This enriching experience spanned two years, after which I returned to my alma mater, SEU.

欢迎对机器学习、深度学习、弱监督学习、可信人工智能有兴趣的同学加入我们一起工作！

对学生的期望：编程基础扎实，有责任心，自驱力强，脚踏实地，勇于挑战

对学生的承诺：平等与尊重的环境，无科研训练外其他事务，共同努力探索知识的边界

More Information:

Personal Webpage

Selected Publications:

1. 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.

2. 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, 46(5):2569–2583, 2024.

3. 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.

4. 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.

5. 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.

6. 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 2022), pp. 3615--3621, Vienna, Austria, Jul 23--29, 2022.

7. Z. Wu, J. Lv, and M. Sugiyama. Learning with Proper Partial Labels. Neural Computation, vol. 35, no. 1, pp. 58--81, 2023.

8. 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.

9. 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.

10. 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.

11. 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.