9 citations · 12 across the 18 of their papers we have counts for
18 papers
The Reward Model Selection Crisis in Personalized Alignment
Fady Rezk, Yuangang Pan, Chuan-Sheng Foo +4
Personalized alignment from preference data has focused primarily on improving personal reward model (RM) accuracy, with the implicit assumption that better preference ranking tran…
Leveraging Scene Context with Dual Networks for Sequential User Behavior Modeling
Xu Chen, Yunmeng Shu, Yuangang Pan +6
Modeling sequential user behaviors for future behavior prediction is crucial in improving user's information retrieval experience. Recent studies highlight the importance of incorp…
Learning Multi-Branch Cooperation for Enhanced Click-Through Rate Prediction at Taobao
Xu Chen, Zida Cheng, Yuangang Pan +6
Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strengt…
Sanitized Clustering against Confounding Bias
Yinghua Yao, Yuangang Pan, Jing Li +2
Real-world datasets inevitably contain biases that arise from different sources or conditions during data collection. Consequently, such inconsistency itself acts as a confounding…
Earning Extra Performance from Restrictive Feedbacks
Jing Li, Yuangang Pan, Yueming Lyu +3
Many machine learning applications encounter a situation where model providers are required to further refine the previously trained model so as to gratify the specific need of loc…
Coarse-to-Fine Contrastive Learning on Graphs
Peiyao Zhao, Yuangang Pan, Xin Li +3
Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised ma…
Taming Overconfident Prediction on Unlabeled Data from Hindsight
Jing Li, Yuangang Pan, Ivor W. Tsang
Minimizing prediction uncertainty on unlabeled data is a key factor to achieve good performance in semi-supervised learning (SSL). The prediction uncertainty is typically expressed…
TRIP: Refining Image-to-Image Translation via Rival Preferences
Yinghua Yao, Yuangang Pan, Ivor W. Tsang +1
Relative attribute (RA), referring to the preference over two images on the strength of a specific attribute, can enable fine-grained image-to-image translation due to its rich sem…
Differential-Critic GAN: Generating What You Want by a Cue of Preferences
Yinghua Yao, Yuangang Pan, Ivor W. Tsang +1
This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset posse…
Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation
Xu Chen, Ya Zhang, Ivor Tsang +2
Cross domain recommendation (CDR) is one popular research topic in recommender systems. This paper focuses on a popular scenario for CDR where different domains share the same set…
Learning Node Representations against Perturbations
Xu Chen, Yuangang Pan, Ivor Tsang +1
Recent graph neural networks (GNN) has achieved remarkable performance in node representation learning. One key factor of GNN's success is the \emph{smoothness} property on node re…
Multi-view Alignment and Generation in CCA via Consistent Latent Encoding
Yaxin Shi, Yuangang Pan, Donna Xu +1
Multi-view alignment, achieving one-to-one correspondence of multi-view inputs, is critical in many real-world multi-view applications, especially for cross-view data analysis prob…
Secure Metric Learning via Differential Pairwise Privacy
Jing Li, Yuangang Pan, Yulei Sui +1
Distance Metric Learning (DML) has drawn much attention over the last two decades. A number of previous works have shown that it performs well in measuring the similarities of indi…
Domain-adversarial Network Alignment
Huiting Hong, Xin Li, Yuangang Pan +1
Network alignment is a critical task to a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain represe…
Probabilistic CCA with Implicit Distributions
Yaxin Shi, Yuangang Pan, Donna Xu +1
Canonical Correlation Analysis (CCA) is a classic technique for multi-view data analysis. To overcome the deficiency of linear correlation in practical multi-view learning tasks, v…
Fast and Robust Rank Aggregation against Model Misspecification
Yuangang Pan, Weijie Chen, Gang Niu +2
In rank aggregation (RA), a collection of preferences from different users are summarized into a total order under the assumption of homogeneity of users. Model misspecification in…
Label Embedding with Partial Heterogeneous Contexts
Yaxin Shi, Donna Xu, Yuangang Pan +2
Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, the…
Millionaire: A Hint-guided Approach for Crowdsourcing
Bo Han, Quanming Yao, Yuangang Pan +4
Modern machine learning is migrating to the era of complex models, which requires a plethora of well-annotated data. While crowdsourcing is a promising tool to achieve this goal, e…