8 citations · 9 across the 5 of their papers we have counts for
5 papers
Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains
Zhuo He, Shuang Li, Wenze Song +4
Endowing deep models with the ability to generalize in dynamic scenarios is of vital significance for real-world deployment, given the continuous and complex changes in data distri…
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
Zhaocheng Liu, Zhongxiang Fan, Jian Liang +2
The one-epoch overfitting phenomenon has been widely observed in industrial Click-Through Rate (CTR) applications, where the model performance experiences a significant degradation…
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
Hanjie Li, Changsheng Li, Kaituo Feng +3
Graph structured data often possess dynamic characters in nature. Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling graph da…
Pareto Domain Adaptation
Fangrui Lv, Jian Liang, Kaixiong Gong +5
Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source. To achieve…
Incentive Compatible Pareto Alignment for Multi-Source Large Graphs
Jian Liang, Fangrui Lv, Di Liu +5
In this paper, we focus on learning effective entity matching models over multi-source large-scale data. For real applications, we relax typical assumptions that data distributions…