Lavie’s Homepage

Biography

I’m a Ph.D. candidate working under the guidance of Prof. Chuan-Xian Ren in the School of Mathematics, SYSU since 2018. I am interested in mathematical and statistical methods for distribution shift and transfer learning, including learning theory, kernel methods, and optimal transport. I received my B.S. degree under the supervision of Prof. Gang Wu from CUMT, where I focused on the application of matrix theory.

Education

Ph.D. in Applied Math, 2018 -
School of Mathematics, Sun Yat-Sen University, Guangzhou, China.
Prof. Chuan-Xian Ren

B.S. in Statistics, 2014 - 2018
School of Mathematics, China University of Mining and Technology, Xuzhou, China.
Prof. Gang Wu

Publications

MOT: Masked Optimal Transport for Partial Domain Adaptation
You-Wei Luo and Chuan-Xian 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
Chuan-Xian Ren*, You-Wei Luo, and Dao-Qing 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
You-Wei Luo and Chuan-Xian 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.”
Unsupervised Domain Adaptation via Discriminative Manifold Propagation
You-Wei Luo, Chuan-Xian Ren*, Dao-Qing Dai, and Hong 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.”
On General Matrix Exponential Discriminant Analysis Methods for High Dimensionality Reduction
Wenya Shi, You-Wei Luo, and Gang Wu*.
Calcolo . 2020, 57(2).
[Springer] [Preprint]
“We develop a general framework of fast implementation on general matrix exponential-based graph embedding methods, and provide robust analysis for the fast computation strategy from a theoretical point of view.”
Enhanced Transport Distance for Unsupervised Domain Adaptation
Mengxue Li, Yi-Ming Zhai, You-Wei Luo, Peng-Fei Ge and Chuan-Xian Ren*.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 2020.
[CVF] [IEEE] [Poster] [Slides] [Code]
Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment
You-Wei Luo, Chuan-Xian Ren*, Pengfei Ge, Ke-Kun Huang and Yu-Feng 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.”
Discriminative Residual Analysis for Image Set Classification With Posture and Age Variations
Chuan-Xian Ren*, You-Wei Luo, Xiao-Lin Xu, Dao-Qing Dai, and Hong Yan.
IEEE Transactions on Image Processing (TIP). 2020, 29: 2875-2888.
[IEEE] [arXiv] [Code]
“DRA explores a powerful projection, which casts the residual representations into a discriminant subspace, to magnify the useful information and discriminability of the input space as much as possible.”

Academic Service

Reviewer or PC Member:

IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
International Conference on Machine Learning (ICML): 2022 (Outstanding Top 10%), 2023
Advances in Neural Information Processing Systems (NeurIPS): 2022
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): 2023
IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)