Lavie’s Homepage

About Me

I am currently a distinguished associate research fellow at the Department of Statistics, Beijing Normal University at Zhuhai. Before joining BNU, I did postdoctoral research and finished my Ph.D. degree in Applied Math at SYSU. Previously, I received my B.S. degree in Statistics from CUMT.

Research Interests

My research focuses on statistical machine learning, including statistical learning theory for transfer learning, statistical perspectives of optimal transport, kernel theory, and applications in computer vision and COPs.

I'm always looking for self-motivated graduate/undergraduate students. Please feel free to contact me if you are interested in my research directions.

Selected Publications

Dataset Shift and Generalization Analysis:

When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights
Y. Luo and C. Ren*.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2024, 46(12): 9407-9422.
[IEEE] [arXiv]
“A systematic study of invariant representation learning with GLS correction, where the theoretical sufficiency and necessity are provided.”
Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation
Y. Luo, C. Ren*, X. Xu and Q. Liu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2024, 46(12): 8727-8742.
[IEEE] [arXiv]
“The co-regularization between discriminability and transferability, which ensures the existence of optimal representations with simultaneously maximized two abilities.”
Unsupervised Domain Adaptation via Discriminative Manifold Propagation
Y. Luo, C. Ren*, D. Dai and H. Yan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2022, 44(3): 1653-1669.
[IEEE] [arXiv]
“We propose a unified manifold learning framework for the UDA and PDA problems, and prove the error bounds with the metrics on the different types of manifolds for both DA settings.”
Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment
Y. Luo, C. Ren*, P. Ge, K. Huang and Y. Yu.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI Oral). 2020.
[AAAI] [arXiv] [Slides] [Code]
“DRMEA describes the domains by a sequence of abstract manifolds, and develops a Riemannian manifold learning framework to achieve transferability and discriminability consistently.”

Optimal Transport:

MOT: Masked Optimal Transport for Partial Domain Adaptation
Y. Luo and C. Ren*.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023.
[CVF] [Preprint] [Video]
“A novel mechanism to overcome strict marginal constraints in OT and achieve conditional transfer.”
BuresNet: Conditional Bures Metric for Transferable Representation Learning
C. Ren*, Y. Luo and D. Dai.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2023, 45(4): 4198-4213.
[IEEE]
“A plug-and-play discrepancy optimization module for transfer learning scenarios, e.g., domain adaptation and few-shot learning.”
Conditional Bures Metric for Domain Adaptation
Y. Luo and C. Ren*.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
[CVF] [Preprint] [Poster] [Video] [Code]
“We develop a theoretical conditional distribution discrepancy called Conditional Kernel Bures (CKB) metric, and propose a conditional invariant feature learning model for UDA.”
Towards Unsupervised Domain Adaptation via Domain-Transformer
C. Ren*, Y. Zhai, Y. Luo and H. Yan.
International Journal of Computer Vision (IJCV) . 2024.
[Springer]
“We connect the core mechanism of Transformer with the optimal transport, where the generalization error can be controlled by the cross-domain Wasserstein distance.”

Academic Service

Reviewer or PC Member:

IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)
Machine Learning (MLJ)
International Conference on Machine Learning (ICML): 2022 (Outstanding Top 10%), 2023, 2024, 2025
Advances in Neural Information Processing Systems (NeurIPS): 2022, 2023, 2024, 2025
International Conference on Learning Representations (ICLR): 2024, 2025
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): 2023, 2024, 2025
International Conference on Computer Vision (ICCV): 2025
European Conference on Computer Vision (ECCV): 2024
International Conference on Artificial Intelligence and Statistics (AISTATS): 2025
AAAI Conference on Artificial Intelligence (AAAI): 2025, 2026
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD): 2025 (Excellent Top 20%)
ACM Multimedia (ACM MM): 2025
European Conference on Machine Learning (ECML): 2025
IEEE/CVF Winter Conference on Applications (WACV): 2026

All Publications

*: Corresponding Author.
[ICML’25] M. Pan, G. Lin, Y. Luo*, B. Zhu, Z. Dai, L. Sun, C. Yuan*. Preference Optimization for Combinatorial Optimization Problems. ICML, 2025. [arXiv]
[ICASSP’25a] Y. Luo, Z. Li, C. Ren*. MPOT: Manifold Preserving Optimal Transport for Visual Recognition Under Severe Distribution Shift. ICASSP (Oral), 2025. [IEEE]
[ICASSP’25b] Y. Luo, Y. Zhai, C. Ren*. Invariant Model Learning on Local-Aware Wasserstein Geodesic for Domain Adaptation. ICASSP, 2025. [IEEE]
[TPAMI’24b] Y. Luo, C. Ren*. When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights. IEEE TPAMI, 2024, 46(12): 9407-9422. [IEEE] [arXiv]
[TPAMI’24a] Y. Luo, C. Ren*, Xiao-Lin Xu, Qingshan Liu. Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation. IEEE TPAMI, 2024, 46(12): 8727-8742. [IEEE] [arXiv]
[IJCV’24] C. Ren*, Y. Zhai, Y. Luo, H. Yan. Towards Unsupervised Domain Adaptation via Domain-Transformer. IJCV, 2024. [Springer]
[ECCV’24] H. Yang, C. Ren*, Y. Luo. COD: Learning Conditional Invariant Representation for Domain Adaptation Regression. ECCV (Oral), 2024. [arXiv]
[AAAI’24] Y. Wang, C. Ren*, Y. Zhai, Y. Luo, H. Yan. Probability-Polarized Optimal Transport for Unsupervised Domain Adaptation. AAAI, 2024. [AAAI]
[SCIS’24] Y. Zhai, C. Ren*, Y. Luo, D. Dai. Maximizing Conditional Independence for Unsupervised Domain Adaptation. SCIS, 2024, 67(5): 152108. [Springer]
[CVPR’23] Y. Luo, C. Ren*. MOT: Masked Optimal Transport for Partial Domain Adaptation. CVPR, 2023. [CVF] [Preprint] [Video]
[TPAMI’23] C. Ren*, Y. Luo, D. Dai. BuresNet: Conditional Bures Metric for Transferable Representation Learning. IEEE TPAMI, 2023, 45(4): 4198-4213. [IEEE]
[TPAMI’22] Y. Luo, C. Ren*, D. Dai, H. Yan. Unsupervised Domain Adaptation via Discriminative Manifold Propagation. IEEE TPAMI, 2022, 44(3): 1653-1669. [IEEE] [arXiv]
[CVPR’21] Y. Luo, C. Ren*. Conditional Bures Metric for Domain Adaptation. CVPR, 2021. [CVF] [Preprint] [Poster] [Video] [Code]
[AAAI’20] Y. Luo, C. Ren*, P. Ge, K. Huang, Y. Yu. Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment. AAAI (Oral), 2020. [AAAI] [arXiv] [Slides] [Code]
[TIP’20] C. Ren*, Y. Luo, X. Xu, D. Dai, H. Yan. Discriminative Residual Analysis for Image Set Classification With Posture and Age Variations. IEEE TIP, 2020, 29: 2875-2888. [IEEE] [arXiv] [Code]
[Calcolo’20] W. Shi, Y. Luo, G. Wu*. On General Matrix Exponential Discriminant Analysis Methods for High Dimensionality Reduction. Calcolo, 2020, 57(2). [Springer] [Preprint]
[CVPR’20] M. Li, Y. Zhai, Y. Luo, P. Ge, C. Ren*. Enhanced Transport Distance for Unsupervised Domain Adaptation. CVPR, 2020. [CVF] [IEEE] [Poster] [Slides] [Code]