100 citations · 134 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
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…