100 citations · 150 across the 13 of their papers we have counts for
13 papers
Partial Domain Adaptation via Importance Sampling-based Shift Correction
Cheng-Jun Guo, Chuan-Xian Ren, You-Wei Luo +2
Partial domain adaptation (PDA) is a challenging task in real-world machine learning scenarios. It aims to transfer knowledge from a labeled source domain to a related unlabeled ta…
Preference Optimization for Combinatorial Optimization Problems
Mingjun Pan, Guanquan Lin, You-Wei Luo +4
Reinforcement Learning (RL) has emerged as a powerful tool for neural combinatorial optimization, enabling models to learn heuristics that solve complex problems without requiring…
COD: Learning Conditional Invariant Representation for Domain Adaptation Regression
Hao-Ran Yang, Chuan-Xian Ren, You-Wei Luo
Aiming to generalize the label knowledge from a source domain with continuous outputs to an unlabeled target domain, Domain Adaptation Regression (DAR) is developed for complex pra…
Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation
You-Wei Luo, Chuan-Xian Ren, Xiao-Lin Xu +1
To overcome the restriction of identical distribution assumption, invariant representation learning for unsupervised domain adaptation (UDA) has made significant advances in comput…
When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights
You-Wei Luo, Chuan-Xian Ren
As a crucial step toward real-world learning scenarios with changing environments, dataset shift theory and invariant representation learning algorithm have been extensively studie…
Maximizing Conditional Independence for Unsupervised Domain Adaptation
Yi-Ming Zhai, You-Wei Luo
Unsupervised domain adaptation studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different distributions. Existing methods mainly fo…
Generalized Label Shift Correction via Minimum Uncertainty Principle: Theory and Algorithm
You-Wei Luo, Chuan-Xian Ren
As a fundamental problem in machine learning, dataset shift induces a paradigm to learn and transfer knowledge under changing environment. Previous methods assume the changes are i…
Towards Unsupervised Domain Adaptation via Domain-Transformer
Ren Chuan-Xian, Zhai Yi-Ming, Luo You-Wei +1
As a vital problem in pattern analysis and machine intelligence, Unsupervised Domain Adaptation (UDA) attempts to transfer an effective feature learner from a labeled source domain…
Geometry-Aware Unsupervised Domain Adaptation
You-Wei Luo, Chuan-Xian Ren, Zi-Ying Chen
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain in the presence of dataset shift. Most existing me…
Conditional Bures Metric for Domain Adaptation
You-Wei Luo, Chuan-Xian Ren
As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA) has attracted widespread attention in recent years. Previous UDA methods assume the mar…
Unsupervised Domain Adaptation via Discriminative Manifold Propagation
You-Wei Luo, Chuan-Xian Ren, Dao-Qing Dai +1
Unsupervised domain adaptation is effective in leveraging rich information from a labeled source domain to an unlabeled target domain. Though deep learning and adversarial strategy…
Discriminative Residual Analysis for Image Set Classification with Posture and Age Variations
Chuan-Xian Ren, You-Wei Luo, Xiao-Lin Xu +2
Image set recognition has been widely applied in many practical problems like real-time video retrieval and image caption tasks. Due to its superior performance, it has grown into…
Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment
You-Wei Luo, Chuan-Xian Ren, Pengfei Ge +2
Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strate…